diff --git a/.gitignore b/.gitignore
index 2d4daa40..b10466ee 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,10 @@
 .idea*
+adavaria/mlband/__pycache__*
+adavaria/data*
+**/*.pyc
+adavaria/results/*
+adavaria/build/*
+adavaria/mlband.egg-info/*
+adavaria/dist/*
+.DS_Store
+
diff --git a/adavaria/ML-Band-Gaps.md b/adavaria/ML-Band-Gaps.md
new file mode 100644
index 00000000..2ccb5adc
--- /dev/null
+++ b/adavaria/ML-Band-Gaps.md
@@ -0,0 +1,31 @@
+# ML Band Gaps (Materials)
+
+> Ideal candidate: skilled ML data scientist with solid knowledge of materials science.
+
+# Overview
+
+The aim of this task is to create a python package that implements automatic prediction of electronic band gaps for a set of materials based on training data.
+
+# User story
+
+As a user of this software I can predict the value of an electronic band gap after passing training data and structural information about the target material.
+
+# Requirements
+
+- suggest the bandgap values for a set of materials designated by their crystallographic and stoichiometric properties
+- the code shall be written in a way that can facilitate easy addition of other characteristics extracted from simulations (forces, pressures, phonon frequencies etc)
+
+# Expectations
+
+- the code shall be able to suggest realistic values for slightly modified geometry sets - eg. trained on Si and Ge it should suggest the value of bandgap for Si49Ge51 to be between those of Si and Ge
+- modular and object-oriented implementation
+- commit early and often - at least once per 24 hours
+
+# Timeline
+
+We leave exact timing to the candidate. Must fit Within 5 days total.
+
+# Notes
+
+- use a designated github repository for version control
+- suggested source of training data: materialsproject.org
diff --git a/example-github-username/README.md b/adavaria/README.md
similarity index 100%
rename from example-github-username/README.md
rename to adavaria/README.md
diff --git a/adavaria/data.ipynb b/adavaria/data.ipynb
new file mode 100644
index 00000000..20a1a73b
--- /dev/null
+++ b/adavaria/data.ipynb
@@ -0,0 +1,352 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mlband.mp as MP\n",
+    "from mlband.imports import *\n",
+    "from mlband import plot_formatter\n",
+    "import mlband.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mp_api.client import MPRester"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'mlband.data' from '/Users/ali/GitHub/rewotes/adavaria/mlband/data.py'>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "importlib.reload(MP)\n",
+    "importlib.reload(mlband.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting the list of materials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Retrieving SummaryDoc documents: 100%|██████████| 20000/20000 [00:19<00:00, 1049.67it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of missing values:  2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df, data = mlband.data.get_list_of_materials(num_chunks=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>material_id</th>\n",
+       "      <th>band_gap</th>\n",
+       "      <th>structure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>mp-757220</td>\n",
+       "      <td>5.3849</td>\n",
+       "      <td>[[6.53586997 0.39866238 2.62533396] Lu, [6.928...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>mp-573369</td>\n",
+       "      <td>3.7419</td>\n",
+       "      <td>[[ 1.63145925  0.23570193 10.77716588] Cs, [1....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>mp-28967</td>\n",
+       "      <td>0.7792</td>\n",
+       "      <td>[[4.55529863 5.11963834 2.01471426] Ba, [1.801...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>mp-559295</td>\n",
+       "      <td>2.6721</td>\n",
+       "      <td>[[0.57211092 2.00312375 5.90744327] Fe, [3.888...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mp-1293179</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>[[-8.73143724 -5.0599669  -7.23635549] Li, [-2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19993</th>\n",
+       "      <td>mp-2715949</td>\n",
+       "      <td>1.5098</td>\n",
+       "      <td>[[-2.79248853  4.83813866  5.78084703] Na, [6....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19994</th>\n",
+       "      <td>mp-1207979</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>[[ 1.99364737 -7.0085493   0.        ] U, [1.9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19995</th>\n",
+       "      <td>mp-1218689</td>\n",
+       "      <td>0.7712</td>\n",
+       "      <td>[[2.982158   1.68966376 0.        ] Sr, [ 2.98...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19996</th>\n",
+       "      <td>mp-2222762</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>[[3.11575461 3.11575461 3.11575461] Sm, [1.341...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19997</th>\n",
+       "      <td>mp-1247060</td>\n",
+       "      <td>0.0535</td>\n",
+       "      <td>[[4.52454226 3.10094945 7.83685747] Dy, [4.524...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19998 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      material_id  band_gap                                          structure\n",
+       "0       mp-757220    5.3849  [[6.53586997 0.39866238 2.62533396] Lu, [6.928...\n",
+       "1       mp-573369    3.7419  [[ 1.63145925  0.23570193 10.77716588] Cs, [1....\n",
+       "2        mp-28967    0.7792  [[4.55529863 5.11963834 2.01471426] Ba, [1.801...\n",
+       "3       mp-559295    2.6721  [[0.57211092 2.00312375 5.90744327] Fe, [3.888...\n",
+       "4      mp-1293179    0.0000  [[-8.73143724 -5.0599669  -7.23635549] Li, [-2...\n",
+       "...           ...       ...                                                ...\n",
+       "19993  mp-2715949    1.5098  [[-2.79248853  4.83813866  5.78084703] Na, [6....\n",
+       "19994  mp-1207979    0.0000  [[ 1.99364737 -7.0085493   0.        ] U, [1.9...\n",
+       "19995  mp-1218689    0.7712  [[2.982158   1.68966376 0.        ] Sr, [ 2.98...\n",
+       "19996  mp-2222762    0.0000  [[3.11575461 3.11575461 3.11575461] Sm, [1.341...\n",
+       "19997  mp-1247060    0.0535  [[4.52454226 3.10094945 7.83685747] Dy, [4.524...\n",
+       "\n",
+       "[19998 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'mlband.data' from '/Users/ali/GitHub/rewotes/adavaria/mlband/data.py'>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "importlib.reload(mlband.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ali/mambaforge/envs/cgcnn/lib/python3.11/site-packages/pymatgen/core/periodic_table.py:120: UserWarning: No Pauling electronegativity for Ar. Setting to NaN. This has no physical meaning, and is mainly done to avoid errors caused by the code expecting a float.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlband.data.create_dataset(df=df, path='data/mp/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AD2sCJlpvxNdYeiO+SpIEv98Po9GotMYSGcP2dKU34mssvRFfY+kF4scwEj+e52PKTjaGsa5NxNeu9CYqhpG6mp6eDkEQEh7DntTD7izODGhgHHUgEMCePXtw++23q/aXl5ejvLwcLpcL06ZNQ1lZGXieR1NTE44ePYrS0lKlbGlpKaqqquB2uyHLclx5x/XlAGD58uVYvHhx1P66ujplcVgAsFgscDqdCIfDqK2tVZUNh8MY7RKw87iEEU4D7GZ1wv2yXkJ1C4Mrg8cIp3oRzxN+GftqwzBwwEWF0ZdsS5UIUQaGOQxR57Xb7cjMzEQgEEBDQ4NKZjKZkJ+fr/jSscIVFBTAaDTC4/GgpaVFJcvMzITdbkcoFEJ9fT3C4bAyNtVkMqGwsBAAFFl7cnNzkZaWBq/XC6/Xq5JlZGTA4XBAkqQoXziOU97eamxsjKrkTqcTFosFra2taG5uVsnS09ORk5MDWZaj9AJAUVEROI5Dc3MzAoGASpadnQ2r1Qqfz4cjR44gMzNTWWi1fQxj6XW5XBAEAW63W7UILdA2Dt9msykxbI8gCEpDor6+PiqR5OXlwWw2w+fzRcXQarUiOzs7Zgx5nkdRUREAoKGhISox2mw2uN1uJY7ticQwVv0GoFybpqYmVcMG+CGGfr8fTU1NKpnZbFaWVYsXw+bmZtX9FrE3KysLwWAwarFjo9GIgoICAOoYRurq4MGDYbVa4fV64fP5VMdG6rcoiqirq1PJ2sfwxIkTUYk8Ur99Pp/SqIvQMUd0vHadkdKJmjGGGTNmYOjQoUoLHADmzJmDp556Cg6HA3v37sWtt94KnudRVlamBLz9T5vIZ6/XqySjzuSxEvXDDz+M+++/X9mOvE2Ul5eneuEl0soyGAzKzRuhLam0rW79jxNSzBY1AFS3yGgOqhOmJLdthxmw83j0t7v0/T38r8Zw1HkjCSUtLS1K1n6Qfaz1ByMvA2RlZcFqtcbUG0lUoVAIjDHk5OTAbDYr5XJzc2O2OIC2m6H9en/ADzEUBCHK3vY4HI5O9VosFpUN7fXyPB9TbyQWdrs9ZmsQaIuh1WpVreHXPoax9EaOtdlsUW+fxUr2He0BEHPtxYivVqu11zF0Op1RvkaSmcViibI3Xv1uT3Z2dqcxTE9Pj1r/sLsxtNvtUS+YRWRms7nbMYzU1fb1MCMjQ3VsxFej0RjX15ycnE7rodVqjVqAuWMMYy3QHIuUTdSMMdx77704cOAANm/erOr4P/fcc5XPo0ePxkMPPYTy8nKUlZUpCcXtdisrQLvdbgBtFyRSETuTx8JsNkfd+EDbDRZr0U2O4+IuxtkiAkDs9RpEGRBDna/l4Ikja5XQ6Xl5no9rUzxZvLe32usVBAEmk0m1Knb7zx2J/GyNRVcxTIbeyM/Wzq57ImIYi77SG8vXyK8Ug8HQ6bHJujaJjGGke+Jk9Z6sr91dtDclH3syxnDfffdhz5492LRpU8xWbnvaJ/Hs7GwUFxejoqJC2VdRUYGSkhLYbLYu5QRBEKlG0hK1JEkIBAJKR34gEFC+zWfNmoXPPvsMn3zySczl69966y14PB4wxrBv3z6sWLECN998syKfOnUqli5dipqaGtTU1GDZsmWYMWNGt+V9RUhmYJ20pPWAloY79RbyUR9ozcekrZn42GOPRT2gu+SSS/Daa69h0KBBMJvNqp8kd9xxB1544QUAwNixY/HVV19BkiQUFRVh+vTpmDt3rhJ8URTx61//GuvXrwcATJo0CatWrVL0dSXvCo/Hozx06cnseYMe+rDbZXtLsobnEQTRc7qbS2hx215AiZogiETQ3Vyirfa/hhFFEWOLBWR0/uxB04iiiOrq6pjjYPUC+agPtOgjJep+gjEGi8BFDcvTC4wxSJIUNVRJT5CP+kCLPlKiJgiCSHEoURMEQaQ4lKgJgiBSHErU/YQgCNhXK6FF1E6/WE8QBAG5ubkpswZdX0A+6gMt+qgdSzUOz/M44ddnkgba/EtLS0u2GX0K+agPtOgjtaj7iXA4jCF2HqbYr/5rnnA4DLfbHTVTnp4gH/WBFn2kRN1PtCVqA9IM+hyeFw6H4fF4NFX5ewr5qA+06CMlaoIgiBSHEjVBEESKQ4maIAgixaFE3U/wPI/jLTJEWZ8jP3ieh8Vi0dz0kT2BfNQHWvSRhuf1E4Ig4Kt67Ty86CmCIMDpdCbbjD6FfNQHWvRRO18pGqdtUiaA1+egD01OdNNTyEd9oEUfKVH3E23TnBphNeozU2tx6sieQj7qAy36SImaIAgixaFETRAEkeJQoiYIgkhxKFETBEGkODQ8r58wmUz46JB2Hl70FJPJhJKSkmSb0aeQj/pAiz4mrUX9/PPPY9SoUTCbzbjxxhtVMlEUMWvWLDgcDjgcDsyePRuSJPWbnCAIIpVIWqIuLCzEwoULcffdd0fJlixZgh07dqCyshKVlZXYvn07li1b1m/yvkAURYx26XsV8traWk0Neeop5KM+0KKPSUvUN910E2688Ubk5OREydasWYOFCxfC5XLB5XJhwYIFeOWVV/pN3hcwxmA363sV8lAopKmXCHoK+agPtOhjyvVRNzU14ejRoygtLVX2lZaWoqqqCm63G7Is96ncZrNF2RQMBhEMBpVtj8cDAAiFQgiFQsp+nuchCAIYY1Hf1u3LZRgRlbD9EoMoA0YeSBfUMklmaP2+ZybLFJ3ovSEGBsAiqM8DAAaDAQaDAbIsR3XvcBwHo9EYZV8Eo9EIjuMgSRJkWY6rNxQKKf/b6xVFMeqGEAQBPM8jHA5HzQkcL4ZAW/9iX+iN+BpLbyxf+yKG7elKb8TXWHojvsbSC8SPYURXOByOOm8iYhjr2kR87UpvomIYqasRXYmOYU/qYaxrG4uUS9Q+nw8AYLfblX2Rz16vVwlKX8ljJerly5dj8eLFUfvr6urg9/uVbYvFAqfTiXA4jNraWlXZ9hfsrBwBdrM64X5ZL6G6hcGVwWOEU70MzAm/jH21YRg44KLC6Eu2pUqEKAPDHIao89rtdmRmZiIQCKChoUElM5lMyM/PV3zpWOEKCgpgNBrh8XjQ0tKikmVmZsJutyMUCqG+vh7hcBg+nw8cx8FkMqGwsBAAFFl7cnNzkZaWBq/XC6/Xq5JlZGTA4XBAkqQoXziOQ3FxMQCgsbExqpI7nU5YLBa0traiublZJUtPT0dOTg5kWY7SCwBFRUXgOA7Nzc0IBAIqWXZ2NqxWKwKBgOKjwWCIimEsvS6XC4IgwO12o7W1VSXLysqCzWZTYtgeQRDgcrkAtMWwYyLJy8uD2WyGz+eLiqHVakV2dnbMGPI8j6KiIgBAQ0NDVGKM1P/W1tYoeyMxjFW/ASjXpqmpSdWwAX6Iod/vR1NTk0pmNpuRl5cHIH4Mm5ubVfdbxN6srCwEg0GcOHFCJTMajSgoKACgjmGkrkYSsNfrVfJOhEj9FkURdXV1Kln7GJ44cSIqkUfqt8/nUxp1ETrmiI7XrjNSLlFbrVYAgNvtVrpF3G43gLbgRYLdV/JYPPzww7j//vuVbY/Hg5KSEuTl5SErK0vZH5mNy2AwKDdvhLak8n8AgH+ckGK2qAGgukVGc1CdMKXvZ9wLM2Dn8ehvd+n7e/hfjeGo80YSSlpaWpSMa2dD5EZpT2Txz6ysLOW6dNQbSVSRn5I5OTkwm81Kudzc3JgtDqAt3haLRSWLxFAQhCh72+NwODrVa7FYVDa018vzfEy9kVjY7faYrUGgLYZWqxU5OTlKi6p9DGPpjRxrs9mi6lesZN/RHqAthh2J+Gq1WnsdQ6fT2WmL2mKxRNkbr363Jzs7u9MYpqenK7GL0N0Y2u121f3WXmY2m7sdw0hdbV8PMzIyVMdGfDUajXF9zcnJ6bQeWq1WpKenx9QbiWFHeWekXKLOzs5GcXExKioqcPrppwMAKioqUFJSonzb97W8I2azOerGB9pusI6VDoDSqmyPIAj4sl5SujiA2P1jogyIoc77zjxxZK0SYtoDtFWQzmRA58cBiLtac0SvIAjIy8tDenq6avrIyE/PWER+tsYiVgzbkwy9JpMppo/t5Z3RnRjGO29f6I3lqyzLcDgcMJvNnU4Dmqxrk6gYRupqZF+iYxihO77G062yo1ul+gBJkhAIBJT+oUAgoPyUnTp1KpYuXYqamhrU1NRg2bJlmDFjhnJsX8v7Ap7nUd0SSdL6g+d5ZGRkaGqO355CPuoDLfqYtBb1kiVLVP2+6enpuOSSS7Bt2zYsWrQIDQ0NGD58OABg0qRJmD9/vlK2r+V9QTgcxoBMHtUtsi6TdTgcht/vR3p6eqetCK1DPuoDLfrIMS2NUUkRPB4PbDYb3G53VJ9ZZ4RCIcxZsw07j0txuy9OlkMrJvSZ7niEQiHU1tYiPz+/2z/ntAb5qA9Sycfu5hLttP0JgiBOUShREwRBpDiUqAmCIFIcStT9BMdxOOGXlTHReoPjOKSlpanGruoN8lEfaNHHlBtHrVeMRiP21ep3FXKj0RjzxQw9QT7qAy36SC3qfoIxBoN2vsB7DGMMsixraqKbnkI+6gMt+kiJup8QRRHjBhpjTqqkB0RRxLFjxzQ1dWRPIR/1gRZ9pERNEASR4lCiJgiCSHEoURMEQaQ4lKgJgiBSHBqe108YjUZsqRKVuaP1htFoRGFhoaZmJOsp5KM+0KKPlKj7CY7jdDlrXoT2q57oFfJRH2jRR+18pWgcSZJwbp4BFp1+NUqSFHNZIj1BPuoDLfpIibqfkGUZeRYeAq/PcdSyLMPv90et7acnyEd9oEUfKVETBEGkOJSoCYIgUhxK1ARBECkOJep+wmAw4F+NYQQk7UwE0xMMBgPsdrvmnqb3BPJRH2jRR52OQUg9DAYDDnm08/CipxgMBmRmZibbjD6FfNQHWvSxVy3qCRNiL6B6/fXXn5QxekaWZeRbOBh1+htGlmW0trZq6kl6TyEf9YEWfexV2ti+fXvM/Tt27DgpY/SMJEk4J09AuqDP4XmSJKGhoUFTY1N7CvmoD7ToY4+6Pp577jkAbfO5Rj5H+Pbbb1FQUJAww6xWq2o7GAxi+PDh+OqrrwAAU6ZMwfr161XLvX/yySe48MILFRvLysqwfv16AMCkSZOwcuVKCILQLTlBEESq0KOstHHjRgBtSS7yGQB4nkd+fj7Wrl2bMMN8Pp9qe+TIkbjttttU+2bOnIlVq1bFPH7JkiXYsWMHKisrAQDjx4/HsmXL8Mgjj3RLThAEkSr0KFFv3boVADB37lw8/fTTfWJQLPbs2YOvv/4aU6ZM6fYxa9aswcqVK+FyuQAACxYswNy5c5VE3JWcIAgiVejV7/xIkm5qaoLX61XJBgwYcPJWdeCVV17B+PHjUVhYqNpfXl6O8vJyuFwuTJs2DWVlZeB5Hk1NTTh69ChKS0uVsqWlpaiqqoLb7YYsy3HlNptNdZ5gMIhgMKhsezweAEAoFEIoFFL28zwPQRDAGIta5kcURXhCDGHGkGEEDB1WQPZLDKIMGHlE9WNLMkPr991psZby8oYYGACLAJU9QNsTboPBAFmWo/rkOI6D0WhUfOmI0WgEx3GQJCnqwUtHvaIogud5iKKo0iuKYtTadIIggOd5hMNhhMPqBX/jxRCA0tWVaL0RX2PpjfjKGFN87IsYtqcrvRFfY+mN+BpLLxA/howxmEwmyLIcdd5ExDDWtYn42pXeRMUwUlcjNiY6hj2ph7GubSx6lag//fRTTJ48GVVVVSojOY6LMuxkaW1txYYNG1BeXq7aP2fOHDz11FNwOBzYu3cvbr31VvA8j7KyMqXbxG63K+Ujn71er2JzZ/KOiXr58uVYvHhxlG11dXXw+/3KtsVigdPpRDgcRm1tbVT5ncfbLvholwC7WZ1wv6yXUN3C4MrgMcKpHt95wi9jX20YBg64qDD6km2pEiHKwDCHIeq8drsdmZmZCAQCaGhoUMlMJhPy8/MVXzpWuIKCAhiNRng8HrS0tKhkmZmZsNvtCIVCqK+vV/Y3NjbCYDAoX6r19fVRdSI3NxdpaWnwer1RX/QZGRlwOByQJCnKF47jUFxcrJynYyV3Op2wWCxobW1Fc3OzSpaeno6cnBzIshzz2hQVFYHjODQ3NyMQCKhk2dnZsFqtyg3d2NgYM4ax9LpcLgiCALfbjdbWVpUsKysLNpstKoZA280e+bVXX18flUjy8vJgNpvh8/miYmi1WpGdnR0zhjzPo6ioCADQ0NAQlRhzcnKQn58Pj8cDt9utkkVi2Fn9jlybpqYmVcMG+CGGfr8fTU1NKpnZbEZeXh6A+DFsbm5W3W8AYLPZkJWVhWAwiBMnTqhkRqNReW4WK4bc940lr9cb1dUaqd+iKKKurk4lax/DWJM7Req3z+dTGnUROuaIjteuMzjWi6V4hw8fjjvuuANTp05FRkaGStYxyZ0sa9euxcMPP4wjR47EfdD3hz/8AeXl5di9ezeamprgcDhw8OBBnH766QCAgwcPYujQoWhuboYsy3Hl3WlRl5SUoL6+HllZWcr+rlocZzzyCQD0aYu64pFxKll/tag706unFnWyYtifLeq+jmEqtKg7+prMGHo8HuTm5sLtdqtySZStnUricOzYMcyfP1/5RupLXn75ZUyePLnL0RjtJwHPzs5GcXExKioqlERcUVGBkpISJQl3JW+P2WyG2WyO2m8ymVSjTiJwHBe1PxQK4cpBRuw6LsEbYgBifz+KMiCGOv/u9MSRtUqIaQ/QFp/OZEDnxwGIG/uI3lAohLq6OuTl5Sk3BwDV545EbrJYxIphe5KhV5IkxcdYOk42hp3RV3pj+dr+OnZ2bLKuTaJi2NHHRMcwQnd8jadbZUe3SnVgwoQJ+PTTT3tzaI84cOAAdu7ciWnTpkXJ3nrrLXg8HjDGsG/fPqxYsQI333yzIp86dSqWLl2Kmpoa1NTUYNmyZZgxY0a35X0BD0Cfo6jb6MWPM81BPuoDrfnYqxZ1ZmYmrr/+ekyYMCFq7PSzzz6bEMOAtoeIP/3pT3HGGWdEyZ5//nn84he/gCRJKCoqwsyZM/HAAw8o8kWLFqGhoQHDhw8H0DZOev78+d2WEwRBpAq9StSiKOKmm24CgKgHA4nkySef7FTWVYveaDRi9erVWL16da/kBEEQqUKvEvWrr76aaDsIgiCITuhVoq6qqupU1hfjqPWA0WjE9mMi/NqZXqBHRIZC6fkVfPJRH2jRx15ZOmjQIHAcp3TItx/9kehx1HqB4zi0RI880g3th0LpFfJRH2jRx16N+mhqakJjYyOamprQ1NSEf/7zn7j11lujXkohfkCSJPw4x4A07XyJ9whJktDY2KipGcl6CvmoD7ToY6/SRsexxjabDX/6059w7rnnYtKkSQkxTG/IsoxiK48qj4xAJ2OotYwsy2hpaYma9VBPkI/6QIs+Jmwa+5aWlqjXYAmCIIiTp1ct6vvvv1+13draio8//hg33nhjImwiCIIg2tGrRN1x7LTVasUjjzyCO+64IyFGEQRBED9A46j7CYPBgP+4wwiG9dc/DfywYKiWVnbuKeSjPtCij70eg7Br1y689tprOHr0KIqLizF58mRlGSwiGoPBgP9r0s5imj3FYDCopo3VI+SjPtCij716mLhhwwZceeWVYIzhpz/9KQDg6quvxhtvvJFQ4/SELMtwpHEw6HRWJlmWEQgENLWyc08hH/WBFn3sVYt6yZIl+PDDDzF27Fhl36RJk3DPPfdg4sSJCTNOT0iShPMLBOw8LsWdqlSrSJKE+vp65Ofnd3vqRq1BPuoDLfrYqxb1sWPHMGbMGNW+iy66CMePH0+IUQRBEMQP9CpRn3nmmXjxxRdV+1566SWMGDEiIUYRBEEQP9Crro9Vq1Zh/PjxWL16NQYNGoRDhw6hrq4O/+///b9E20cQBHHK06NELUkSqqurMWrUKBw8eBAffvghjh49ikmTJuHiiy+OWiWc+AGO4xAIM8gaW1miu3AcB4PB0C/LsyUL8lEfaNHHHiXqlStX4ptvvsGaNWtgs9lw++23K7IZM2Zg+PDhqlVWiB8wGo3YdkQ7k8D0FKPRqPsvavJRH2jRxx71Ua9fvx4PPfRQTNlvfvMbvP766wkxiiAIgviBHiXqw4cPx1y/EADOOOOMuAsKnOqIoohLSwRYtTUNbrcRRRHHjx+HKOp30m3yUR9o0ccej/pobGzs0X6iDcYY0gwceA31i/UExhjC4bDmVnfuCeSjPtCijz1K1KNHj8a6detiyt544w1ccMEFCTGKIAiC+IEePUycP38+rrnmGvh8Ptx2220oKirCsWPHsGHDBqxYsYKG5xEEQfQBPWpRX3zxxVi/fj1efvllDBkyBOnp6RgyZAhefvllrF+/HhdddFHCDJsyZQpMJhOsVqvyt2vXLkUuiiJmzZoFh8MBh8OB2bNnq5bWOVk5QRBEqtDjPuprr70W3377Lf71r39h+/bt+Oabb/Dtt99iwoQJCTdu5syZ8Pl8yl/72fmWLFmCHTt2oLKyEpWVldi+fTuWLVuWMHmiEQQBe2oktIja6RfrCYIgIDc3V1MrO/cU8lEfaNFHjqVoj/qUKVNgt9uxatWqmPKSkhKsXLkSP//5zwEAb7/9NubOnYvDhw8nRN6eYDCIYDCobHs8HpSUlKC+vh5ZWVnKfp7nIQgCGGMxnyif8cgnAIAMI2Do8FDRLzGIMmDkgXRBLZNkhtbvG/tZpuiHkd4QAwNgEYCKR8apZAaDAQaDAbIsR/1iaL8acygUitJrNBrBcRwkSYqaaay7ekVRjHpoIwgCeJ5HOByOWrW+qxhGJtFJtN6Ir7H0JjuGsfRGfI2lN+JrLL1AcmMYS2/E16706jGGHo8Hubm5cLvdqlwSZWunkhSgvLwc5eXlcLlcmDZtGsrKysDzPJqamnD06FGUlpYqZUtLS1FVVQW32w1Zlk9K3nHx3uXLl2Px4sVR9tXV1cHv9yvbFosFTqcT4XAYtbW1qrKyLOOMbB6HPTLOyhFgN6sT7pf1EqpbGFwZPEY41ROan/DL2FcbhoEDLiqMvmRbqkSIMjDMYYg6r91uR2ZmJgKBABoaGlQyk8mE/Px8xZeOFa6goABGoxEejwctLS0qWWZmJux2O0KhEOrr6yHLMoLBIMxms+qFgvr6+qjKmpubi7S0NHi9Xni9XpUsIyMDDocDkiRF+cJxHIqLiwG0jTLqePM5nU5YLBa0traiublZJUtPT0dOTg5kWY7SCwBFRUXgOA7Nzc0IBAIqWXZ2NqxWK1paWlBdXQ2z2Qye56NiGEuvy+WCIAhwu91obW1VybKysmCz2ZQYtkcQBLhcLgBQ4tuevLw8mM1m+Hy+qBharVZkZ2fHjCHP8ygqKgIANDQ0RCXGyHEAovRGYhirfgNQrk1TU5OqYRPRa7Va4ff7o1aIMpvNyMvLAxA/hs3Nzar7DWhbWDsrKwvBYBAnTpxQyYxGIwoKCgCoYxipqyUlJUhPT4fX64XP51MdG6nfoiiirq5OJWsfwxMnTkQl8kj99vl88Hg8KlnHHNExxp2Rsi3qL774AiUlJXA4HNi7dy9uvfVWlJWVoaysDEeOHMGAAQNQX1+PnJwcAG0XIi8vD0eOHAFj7KTkkQoXIREt6lAohLn/sxM7j0sIM6a7FnUk2eTm5irJGtBXizoQCKC6uhq5ubmKHXprUcuyrNwXHVdA0UuLOlJXXS4X0tLSqEV9Mpx77rnK59GjR+Ohhx5CeXk5ysrKlGXe3W63kmjdbjeAtm/CSNB7K++I2WyG2WyO2m8ymWLOZ8txXNx5bltEAIj9/SjKgBhnvup4c1m3Suj0vDzPx7UpnixeX157vYIgwGQyKTcHANXnjkRuslh0FcNk6I3cZJ1d90TEMBZ9pTeWr5GEZjAYOj02WdcmkTGMJNOT1XuyvnZ3PuxeTXOaDCJBBdp+RhUXF6OiokLZV1FRgZKSEthstpOWEwRBpBIpm6jfeusteDweMMawb98+rFixAjfffLMinzp1KpYuXYqamhrU1NRg2bJlmDFjRsLkBEEQqULKdn08//zz+MUvfgFJklBUVISZM2eqZuZbtGgRGhoaMHz4cABtS4HNnz8/YfJEw/M8jvpkhOSUfCRw0vA8j4yMDNUvH71BPuoDLfqYsg8TUxmPxwObzdblA4CODHrowz60qo1DKxI/np0giL6hu7lEO18pGocxhgwjwOtzTiblKbaev/fJR32gRR8pUfcToijip0VGWI36zNSiKKKmpkZTU0f2FPJRH2jRR0rUBEEQKQ4laoIgiBSHEjVBEESKQ4m6H5HR2fuI+kBLqzr3FvJRH2jNx5QdR603TCYTNh3SzsOLnmIymaLmSNEb5KM+0KKP1KImCIJIcShR9xOiKOKiQgEZOl6FvLa2VlNDnnoK+agPtOgjJep+gjGGLBMXNb2pXmCMIRQKaeolgp5CPuoDLfpIiZogCCLFoURNEASR4lCiJgiCSHEoUfcTgiDg73US/JJ2+sV6giAIcDqdmlrZuaeQj/pAiz5qx1KNw/M8alv1maSBNv8sFkuyzehTyEd9oEUfqUXdT4TDYQzK4mHSacTD4TC8Xm/UYp56gnzUB1r0UadpI/UIh8MY5jAgTdDn8LxwOIzm5mZNVf6eQj7qAy36SImaIAgixaFETRAEkeJQoiYIgkhxKFH3EzzPo65VhqTjVcjT09M1tbJzTyEf9YEWfUxJS4PBIO6++24MHjwYmZmZGDZsGNasWaPIp0yZApPJBKvVqvzt2rVLkYuiiFmzZsHhcMDhcGD27NmQJKnb8r5AEAR8URdGa9+eJmkIgoCcnBxNjU3tKeSjPtCijymZqCVJgsvlwubNm+HxeLB27Vo88MAD2LRpk1Jm5syZ8Pl8yt+FF16oyJYsWYIdO3agsrISlZWV2L59O5YtW9ZteV/AGIORB/Q55qPNv3A4rKmJbnoK+agPtOhjSibqjIwMPP744zj99NPBcRxGjx6Nyy67DDt27OjW8WvWrMHChQvhcrngcrmwYMECvPLKK92W9wWiKOK/BhiRadJnqhZFEcePH9fU1JE9hXzUB1r0URNt/0AggD179uD2229X9pWXl6O8vBwulwvTpk1DWVkZeJ5HU1MTjh49itLSUqVsaWkpqqqq4Ha7IctyXLnNZos6fzAYRDAYVLY9Hg8AIBQKIRQKKft5nocgCGCMRVWC9uUyjIia7tQvMYgyYOSB9A5jrSWZKV0mWTESvTfEwABYBPV5AMBgMMBgMECW5ajuHY7jYDQao+yLYDQawXEcJEmCLMtx9YZCIeV/e72iKEa1XARBAM/zCIfDUWNZ48UQaFudoy/0RnyNpTeWr30Rw/Z0pTfiayy9EV9j6QXixzCiKxwOR503ETGMdW0ivnalN1ExjNTViK5Ex7An9TDWtY1FyidqxhhmzJiBoUOH4qabbgIAzJkzB0899RQcDgf27t2LW2+9FTzPo6ysDD6fDwBgt9sVHZHPXq9XCWpn8liJevny5Vi8eHHU/rq6Ovj9fmXbYrHA6XQiHA6jtrZWVbb9BTsrR4DdrE64X9ZLqG5hcGXwGOE0qGQn/DL21YZh4ICLCqMv2ZYqEaIMDHMYos5rt9uRmZmJQCCAhoYGlcxkMiE/P1/xpWOFKygogNFohMfjQUtLi0qWmZkJu92OUCiE+vp6hMNh+Hw+cBwHk8mEwsJCAFBk7cnNzUVaWhq8Xi+8Xq9KlpGRAYfDAUmSonzhOE5ZQqmxsTGqkjudTlgsFrS2tqK5uVklS09PR05ODmRZjtILAEVFReA4Ds3NzQgEAipZdnY2rFYrAoGA4qPBYIiKYSy9LpcLgiDA7XajtbVVJcvKyoLNZlNi2B5BEOByuQC0xbBjIsnLy4PZbIbP54uKodVqRXZ2dswY8jyPoqIiAEBDQ0NUYozU/9bW1ih7IzGMVb8BKNemqalJ1bABfoih3+9HU1OTSmY2m5GXlwcgfgybm5tV91vE3qysLASDQZw4cUIlMxqNKCgoAKCOYaSuRhKw1+tV8kaESP0WRRF1dXUqWfsYnjhxIiqRR+q3z+dTGnUROuaIjteuMziWwh01jDHce++92L9/PzZv3hwziQLAH/7wB5SXl2P37t1oamqCw+HAwYMHcfrppwMADh48iKFDh6K5uRmyLMeVd7dFXVJSgvr6emRlZSn7u2pRz/2fndh5XEKYsT5rUVc8Mk4l688WdX19PXJzc2E2m3XZog4EAqiurkZubq5ihx5b1PX19cjJyVG+jDrq1UOLur6+Hi6XC2lpaUltUXs8HuTm5sLtdqtySUdStkXNGMN9992HPXv2YMuWLZ0maQCqYTbZ2dkoLi5GRUWFkogrKipQUlKi6OhK3hGz2Qyz2Ry132QyKResPZFWZWe0iEBn65GLMiCGOv/u9MSRtUro9Lw8z8e1KZ4s3tPx9noFQYDJZFJuDgCqzx2J3GSx6CqGydAbuck6u+6JiGEs+kpvLF8jCc1gMHR6bLKuTSJjGEmmJ6v3ZH2Np1tlR7dKJYFZs2bhs88+wyeffILs7GyV7K233oLH4wFjDPv27cOKFStw8803K/KpU6di6dKlqKmpQU1NDZYtW4YZM2Z0W94XGI1GfHJYjJtotYzRaERRUVHciqt1yEd9oEUfU7Lr4/Dhwxg0aBDMZrPq2+6OO+7ACy+8gLFjx+Krr76CJEkoKirC9OnTMXfuXOUbUhRF/PrXv8b69esBAJMmTcKqVasUXV3Ju8Lj8cBms3X5c6Ujgx76sNtlU5lDKyYk2wSC0AXdzSUpmahTnd4kalEUMfH5Lfi6QfsvvcRK1KIoorm5GXa7XVMtlZ5APuqDVPKxu7kkZbs+9AZjDDnpPARen+OoGWMIBAKaeomgp5CP+kCLPlKiJgiCSHFSdtQHkbrE6mvPMnG4qFDAzuP/SMgDU+oHJ4gfoBY1QRBEikOJup8wGAz4uiGs21XI/RLTtX9A2zXMzs7udGysHiAfUxPq+ugnDAYDqrxy1wU1iihD1/4BbdfQarUm24w+hXxMTahF3U/IsgxXBgejTiNu5KFr/4C2a9jS0hL1urGeIB9TE2pR9xOSJOHsXAE7j0txXxHXKukCl1D/+uPloJ4+sJQkCY2NjcjPz+/2q79ag3xMTXTc/iEIgtAHlKgJgiBSHErUBEEQKQ4l6n6C4zg0BxnCGnpttSeEGdO1f8APU1NynD6nAQDIx1SFHib2E0ajEburNT4bUxxaROjaP6DtGkZWc9Er5GNqQi1qgiCIFIda1P1EKBTC1YOM2Hlc0uXiAT/M9aEd/3o6BLA3PmptzpJQKITa2lpNDV3rKVr0kVrUBEEQKQ4laoIgiBSHEjVBEESKQ4maIAgixaE1E3tBb9ZMZIzhzEV/RSAMyDqMOM8BaQbo1j8gdX1M5ANLxhjC4TAMBoOmxhn3hFTysbu5hEZ99BMcx2l+Udt4yAy69g84NXzkOA6CoO+0oEUfqeujn5AkCSNzDUjXVv3oNukCdO0fcGr4KEkSGhoaIEn6/UbSoo86rnLxEUURZWVlWL9+PQBg0qRJWLlyZZ9908qyjMIMHofcMvxIod/NCcLIc7r2D0hdHxM5JWxnY8W1Nh48HrIso7W1FZmZmck2pducsi3qJUuWYMeOHaisrERlZSW2b9+OZcuWJdssgiCIKE7ZRL1mzRosXLgQLpcLLpcLCxYswCuvvJJsswiCIKI4Jbs+mpqacPToUZSWlir7SktLUVVVBbfbDZvNpiofDAYRDAaVbbfbDQBoaGhAKBRS9vM8D0EQwBiDKIoqHaFQCCF/C8LBMNIYg6HD0+aAxCDKbUtapQlqmSQz+L/vTss0RT+l9oUYGNr6UAW++3rDjKH1ezOtJg4dNbeIDDIDzAJg6qA3GGYIhQEDB1iMHNJlgAsZkC6HIcoMLd/rzTACfAdfW0WGMANMBsBsUMtCMkNQahthkWFUy9j3vgKAxYhexZD73teOeL/XGy+GHMcpPoZZdAxjXZtIDNOEtq4Tld4wg9guhu1RXxug49WJxNBsAEydxDCW3q5iyIWAkN8AgxhGRrvenTPnvQNRZgicZAxPpn7Hi6HAt60y1J6O9fvTBy9ti08ohKamJpjNZqSlpUGSpKhluSL3sizLMfuyI6+ei6KIjgPnBEEAz/MIh8MIh8Mx9UZyhNfrBYAoHVGwU5CqqioGgNXX1yv76urqGAB25MiRqPKPPvooQ1sdpz/6oz/6S/hfrLzTnlNyHHVTUxMcDgcOHjyI008/HQBw8OBBDB06FM3NzV22qGVZRmNjI5xOZ7fHYXo8HpSUlODIkSPdHnutJfTuH0A+6oVU8pExBq/Xi8LCQvB85z3Rp2TXR3Z2NoqLi1FRUaEk6oqKCpSUlEQlaQAwm80wm82qfXa7vVfnzsrKSnrl6Ev07h9APuqFVPExVs7pyCn7MHHq1KlYunQpampqUFNTg2XLlmHGjBnJNosgCCKKU7JFDQCLFi1CQ0MDhg8fDqBtHPX8+fOTbBVBEEQ0p2yiNhqNWL16NVavXt0v5zObzXj00UejulD0gt79A8hHvaBFH0/Jh4kEQRBa4pTtoyYIgtAKlKgJgiBSHErUBEEQKQ4laoIgiBSHEnU/IIoiZs2aBYfDAYfDgdmzZ2tqLtx4BINB3H333Rg8eDAyMzMxbNgwrFmzJtlm9Rl+vx9Dhgzp9QtPqc7777+P0tJSZGRkoLCwEC+88EKyTUoox44dw4033gin04mcnBzccsstqK2tTbZZXUKJuh/Q85SqkiTB5XJh8+bN8Hg8WLt2LR544AFs2rQp2ab1CY888giKi4uTbUaf8NFHH2HmzJlYtWoVPB4PKisrcemllybbrIQyc+ZMAMDhw4fx3XffIRgM4le/+lWSreoGiZzsiIhNcXExe/vtt5Xtt956iw0YMCCJFvUtP/vZz9iiRYuSbUbC2b9/PxsxYgT76KOPmM1mS7Y5CWfUqFHsxRdfTLYZfcpZZ53F1q1bp2z/z//8DzvzzDOTaFH3oBZ1H9PVlKp6IxAIYM+ePRg5cmSyTUkokiTh7rvvxurVqzX1okR3aWlpwf79++HxeDBs2DAUFBTgv//7v1FTU5Ns0xLK/fffj7fffhtutxvNzc144403MGFC6q9eQ4m6j/H5fADUkzhFPkfmotULjDHMmDEDQ4cOxU033ZRscxLKM888g5EjR+quKyBCU1MTGGN4/fXX8fHHH+PgwYMwGo248847k21aQhkzZgzq6uqQnZ0Nh8OBxsZGLFy4MNlmdQkl6j7GarUCgKr1HPmspTXbuoIxhnvvvRcHDhzAe++9F3fKRq3x7bffYvXq1Xj66aeTbUqfEamnc+bMwcCBA2G1WrF48WJs2bIFLS0tSbYuMciyjHHjxmHMmDHw+Xzw+Xy4+OKLcdVVVyXbtC7Rz92UorSfUjVCvClVtQhjDPfddx/27NmDTZs26cavCNu3b0d9fT3OPPNMFBQU4KabboLH40FBQQH27NmTbPMSgt1ux4ABA2LOr850MstEY2MjDh8+jDlz5sBiscBisWD27NnYtWsXTpw4kWzz4pPUHvJThEWLFrFzzjmHVVdXs+rqanbOOeewxYsXJ9ushDFz5kw2cuRIduLEiWSb0ie0trYq1666upq9++67LCsri1VXV7NQKJRs8xLGkiVL2Nlnn82OHj3KWltb2V133cWuuOKKZJuVUIYMGcIeeugh5vf7md/vZ/PmzWPFxcXJNqtLKFH3A6FQiM2cOZPZ7XZmt9vZfffdx0RRTLZZCeHQoUMMADObzSwjI0P5++Uvf5ls0/qMrVu36nLUhyRJ7P7772dOp5M5nU7285//nFVXVyfbrIRSWVnJrrzySuZwOJjdbmeXXXYZ++KLL5JtVpfQ7HkEQRApDvVREwRBpDiUqAmCIFIcStQEQRApDiVqgiCIFIcSNUEQRIpDiZogCCLFoURNEASR4lCiJog4NDc3g+M4HDp0KNmmKKxYsQLz5s1LmL5x48Zh8+bNCdNHJB5K1ISmuPTSS2E2m2G1WpGZmYkzzzwTb7/9drLN6jfcbjeeffZZPPjgg12W/fzzz2E0GmNOVbpgwQJccsklAICFCxd2Sx+RPChRE5rjiSeegM/ng8fjwZNPPolJkybh8OHDyTarX3j99dcxduxY5OTkdFn2ggsuwLBhw1BeXq7aL8syysvLMX36dADA2LFj0dzcjM8++6xPbCZOHkrUhGbhOA4TJkyA3W7HgQMHALTN/33DDTcgLy8PNpsNY8eOxZdffqkc89hjj+G6667DrFmzlBnj3nzzTUUeDAZx7733wuFwYPDgwXjnnXfi2hAMBnHPPfco5V955RVVV8mmTZswatQo2Gw2uFwuzJw5E36/Xzl+0KBBWLp0Kc4991xkZWXhqquuwvHjxzs93/vvv4/LL79cte/bb7/Fddddh9zcXAwcOBBLliyBLMsAgOnTp+PVV19Vld+0aRM8Hg9+/vOfK3G8/PLL8f7778f1lUgelKgJzSLLMv73f/8XgUAA55xzjrLv9ttvx3fffYfa2lqcc845uPXWW1VTdX788ccYM2YMGhoasGTJEsyYMUNZxGHp0qXYtWsX/vnPf+Lvf/87/vznP8e1YcmSJdi3bx8qKytRUVGBjRs3quTp6el46aWX0NjYiM8++wxbt27Fs88+qyrz8ssvY/369aipqUFBQQEmTZrU6fkqKiowbNgwZdvv9+O//uu/cPnll+PYsWPYvn07NmzYoCTnO++8E//5z3+wa9cu5Zg1a9Zg4sSJsFgsyr4RI0aopuIlUowkTwpFED3ikksuYWlpacxms7G0tDTG8zx74oknOi3f1NTEALCjR48yxhh79NFH2QUXXKDIZVlmJpOJ7du3jzHG2GmnncbefPNNRb57924GgH333Xcx9Z922mmq9TD37NkTt/zKlStVU4cOHDhQZX9NTQ0DwI4cORLzeEEQ2N69e5Xtt956i5WWlqrK/OlPf2KXX365sn3LLbewGTNmMMYYa2hoYGazme3ZsyfqmJ/85Ccxz0kkH2pRE5pj+fLlaG5uht/vx4EDB/Dqq6/ixRdfBNDWwpw5cyYGDRqErKwsDBo0CABUE8MXFBQonzmOQ3p6utKiPn78OAYOHKjI23+OxfHjx1FSUqJsDxgwQCXfu3cvrrjiCuTn5yMrKwvz58+PmqS+/Tny8/NhNptx7NixmOfLzs6Gx+NRtg8dOoR//vOfsNvtyt8DDzygeoA4ffp0vPnmm2htbcW6detwxhln4Cc/+YlKr8fjQXZ2dlxfieRBiZrQNEOGDMGECRPwwQcfAGhb23D//v3YsWMHPB6P0lfMujmbb2FhoerBZFVVVZfljxw50mn5iRMn4rLLLsN//vMfeDweLFu2LMqW9uerq6tDMBhEUVFRzPOVlpbiX//6l7JdUlKC8847D83Nzcqfx+NBZWWlUmbcuHHIzs7GO++8gzVr1igPEdvz9ddfqxZgJlILStSEpjl8+DD++te/4qyzzgLQ1jJMS0tDdnY2fD4f5s+f3yN9EydOxIoVK3D8+HE0Nzfj8ccf77L8k08+iZqaGrjdbvz2t79VyT0eD+x2OzIyMvDNN9/gj3/8Y5SOF198EQcOHIDf78e8efMwduxYFBcXxzzfddddh61btyrb1157LWpra/GHP/wBgUAA4XAYBw4cwLZt25QyPM9jypQpePTRR/H111/jjjvuiNK7detWXHvttXF9JZIHJWpCc8ybNw9WqxVWqxVjxozBFVdcgUceeQQAcP/998NgMCA/Px8//vGPceGFF/ZI98KFCzFq1Cj8+Mc/RmlpKW688cYuy5999tkYMWIESktLcc011wAAzGYzgLYk/PTTT8NqteKee+7BbbfdFqVj2rRpmDhxIvLz83Hs2DGsW7eu0/Pdeeed+Nvf/oaGhgYAbYvSbt68GVu2bMGgQYPgdDpx++23R42dnjp1Kg4fPowbb7wRTqdTJdu+fTsyMzPx05/+tMv4EMmBVnghiASyc+dOXHrppQgEAt1aiX3QoEFYtWpVl18I7Yn00T/xxBMnYekPXHXVVZg7dy7GjRuXEH1E4hGSbQBBaJm6ujpUVlZi7NixqK2txfz583HzzTd3K0n3locffjih+j7++OOE6iMSD3V9EMRJEA6HUVZWBpvNhrPPPhsulwu///3vk20WoTOo64MgCCLFoRY1QRBEikOJmiAIIsWhRE0QBJHiUKImCIJIcShREwRBpDiUqAmCIFIcStQEQRApDiVqgiCIFIcSNUEQRIrz/wE5cty9r9VJ2AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 354.331x218.989 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df['band_gap'].hist()\n",
+    "ax.set_xlabel('Band gap (eV)')\n",
+    "ax.set_ylabel('Count')\n",
+    "ax.set_title('Band gap distribution in MP')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Retrieving SummaryDoc documents: 100%|██████████| 1000/1000 [00:00<00:00, 35848752.14it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = MP.mpr.materials.summary.search(\n",
+    "    # chemsys=\"Si-O\", \n",
+    "    fields=['material_id', 'composition', 'band_gap', 'structure'],\n",
+    "    chunk_size=1000,\n",
+    "    num_chunks=1,\n",
+    ")\n",
+    "\n",
+    "df = MP.mp_summary_to_df(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of missing values:  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking the missing values\n",
+    "ind = ~(df['band_gap'] >= 0)\n",
+    "print('Number of missing values: ', ind.sum())\n",
+    "# Drop the missing values\n",
+    "df = df.loc[~ind, :]\n",
+    "# Reset the index\n",
+    "df.reset_index(drop=True, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_pickle('data/mp.pkl')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py38",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adavaria/demo.ipynb b/adavaria/demo.ipynb
new file mode 100644
index 00000000..7a820d7f
--- /dev/null
+++ b/adavaria/demo.ipynb
@@ -0,0 +1,818 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A machine learning model for predicting band gap of materials\n",
+    "\n",
+    "Presented by: Ali Davari\n",
+    "\n",
+    "I picked Crystal Graph Convolutional Network model based on [Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties](https://doi.org/10.1103/PhysRevLett.120.145301) paper by [Tian Xie](https://github.com/txie-93) et al. \n",
+    "\n",
+    "In CGCNN, a crystal structure is represented as a graph where nodes correspond to atoms and edges represent atomic bonds or spatial relationships. This graph-based representation captures both local and global structural information of the crystal, which is crucial for predicting material properties. The unique feature of CGCNN is its ability to process varying input sizes and structures, as each crystal can have different numbers of atoms and types of bonding. The input features to a CGCNN typically include physical properties of the atoms, such as atomic number, atomic radius, and electronegativity, which provide essential information about the nature of the atoms and their interactions. Additionally, features can also include information about the bonding environment of each atom, like bond length. These features are then processed through convolutional layers that aggregate information from neighboring nodes (atoms) and edges (bonds), allowing the network to learn complex patterns in the material's structure, which are crucial for predicting various material properties.\n",
+    "\n",
+    "![image](images/cgcnn.png)\n",
+    "Image [Ref.](https://doi.org/10.1103/PhysRevLett.120.145301)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "~~~sh\n",
+    "git clone https://github.com/adavaria/rewotes.git\n",
+    "cd adavaria\n",
+    "conda create -n cgcnn python=3 scikit-learn pytorch ipykernel torchvision pymatgen -c pytorch -c conda-forge -y\n",
+    "conda activate cgcnn\n",
+    "python setup.py install\n",
+    "~~~"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Input dataset preparation:\n",
+    "\n",
+    "To prepare for defining a custom dataset, you'll need the following:\n",
+    "\n",
+    "- **CIF files** detailing the structures of the crystals of interest.\n",
+    "- **Target properties** for each crystal. For prediction purposes, placeholder values in `id_prop.csv` are sufficient.\n",
+    "\n",
+    "To create your custom dataset, set up a directory, `root_dir`, containing:\n",
+    "\n",
+    "- `id_prop.csv`: A CSV file with two columns. The first column should have a unique ID for each crystal, and the second column should record the value of the target property. For use with `predict.py`, any number can be placed in the second column (it's required but can be arbitrary).\n",
+    "\n",
+    "- **ID.cif**: CIF files that record the crystal structure, where `ID` corresponds to the unique ID for the crystal.\n",
+    "\n",
+    "The structure of `root_dir` should be as follows:\n",
+    "~~~\n",
+    "root_dir/\n",
+    "├── id_prop.csv\n",
+    "├── atom_init.json\n",
+    "├── id0.cif\n",
+    "├── id1.cif\n",
+    "├── ...\n",
+    "~~~\n",
+    "\n",
+    "#### Collecting data from MP:\n",
+    "\n",
+    "~~~python\n",
+    "import mlband.data\n",
+    "\n",
+    "print('Downloading the data from MP...' ,flush=True)\n",
+    "# each chunk is 1,000 data points\n",
+    "df, data = mlband.data.get_list_of_materials(num_chunks=1)\n",
+    "print('Creating the dataset...', flush=True)\n",
+    "data_set_path = 'data/mp-test/'\n",
+    "mlband.data.create_dataset(df=df, path=data_set_path)\n",
+    "~~~"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Featerizing the elements\n",
+    "\n",
+    "Below you can see some of the available elemental features fed to the nodes through one-hot-encoding. [Magpie](https://github.com/ramv2/magpie_python) was used to collect these elemental properties. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>atomic number</th>\n",
+       "      <th>atomic mass</th>\n",
+       "      <th>group</th>\n",
+       "      <th>period</th>\n",
+       "      <th>MeltingT</th>\n",
+       "      <th>MiracleRadius</th>\n",
+       "      <th>CovalentRadius</th>\n",
+       "      <th>FirstIonizationEnergy</th>\n",
+       "      <th>GSestFCClatcnt</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GSenergy_pa</th>\n",
+       "      <th>HeatCapacityMolar</th>\n",
+       "      <th>n_ws^third</th>\n",
+       "      <th>HeatFusion</th>\n",
+       "      <th>ZungerPP-r_d</th>\n",
+       "      <th>ZungerPP-r_pi</th>\n",
+       "      <th>NpValence</th>\n",
+       "      <th>Row</th>\n",
+       "      <th>FusionEnthalpy</th>\n",
+       "      <th>GSbandgap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>H</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.007940</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>13.598443</td>\n",
+       "      <td>2.979865</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-3.331291</td>\n",
+       "      <td>28.836</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>0.060</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.060</td>\n",
+       "      <td>7.853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>He</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.002602</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>24.587387</td>\n",
+       "      <td>3.664774</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001135</td>\n",
+       "      <td>20.786</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>18.098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Li</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6.941000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>453.69</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>5.391719</td>\n",
+       "      <td>4.048846</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.869887</td>\n",
+       "      <td>24.860</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.360</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Be</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9.012182</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1560.00</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>9.322700</td>\n",
+       "      <td>3.160184</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-3.755039</td>\n",
+       "      <td>16.443</td>\n",
+       "      <td>1.67</td>\n",
+       "      <td>7.895</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.200</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.895</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10.811000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2348.00</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>8.298020</td>\n",
+       "      <td>3.061330</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-6.677698</td>\n",
+       "      <td>11.087</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>50.200</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.165</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>50.200</td>\n",
+       "      <td>1.524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>Fl</td>\n",
+       "      <td>114</td>\n",
+       "      <td>289.000000</td>\n",
+       "      <td>14</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>Mc</td>\n",
+       "      <td>115</td>\n",
+       "      <td>290.000000</td>\n",
+       "      <td>15</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>Lv</td>\n",
+       "      <td>116</td>\n",
+       "      <td>293.000000</td>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>Ts</td>\n",
+       "      <td>117</td>\n",
+       "      <td>294.000000</td>\n",
+       "      <td>17</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>Og</td>\n",
+       "      <td>118</td>\n",
+       "      <td>294.000000</td>\n",
+       "      <td>18</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>118 rows × 70 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    symbol  atomic number  atomic mass  group  period  MeltingT  \\\n",
+       "0        H              1     1.007940      1       1     14.01   \n",
+       "1       He              2     4.002602     18       1      0.00   \n",
+       "2       Li              3     6.941000      1       2    453.69   \n",
+       "3       Be              4     9.012182      2       2   1560.00   \n",
+       "4        B              5    10.811000     13       2   2348.00   \n",
+       "..     ...            ...          ...    ...     ...       ...   \n",
+       "113     Fl            114   289.000000     14       7      0.00   \n",
+       "114     Mc            115   290.000000     15       7      0.00   \n",
+       "115     Lv            116   293.000000     16       7      0.00   \n",
+       "116     Ts            117   294.000000     17       7      0.00   \n",
+       "117     Og            118   294.000000     18       7      0.00   \n",
+       "\n",
+       "     MiracleRadius  CovalentRadius  FirstIonizationEnergy  GSestFCClatcnt  \\\n",
+       "0              0.0            31.0              13.598443        2.979865   \n",
+       "1              0.0            28.0              24.587387        3.664774   \n",
+       "2            152.0           128.0               5.391719        4.048846   \n",
+       "3            112.0            96.0               9.322700        3.160184   \n",
+       "4             88.0            84.0               8.298020        3.061330   \n",
+       "..             ...             ...                    ...             ...   \n",
+       "113            0.0             0.0               0.000000        0.000000   \n",
+       "114            0.0             0.0               0.000000        0.000000   \n",
+       "115            0.0             0.0               0.000000        0.000000   \n",
+       "116            0.0             0.0               0.000000        0.000000   \n",
+       "117            0.0             0.0               0.000000        0.000000   \n",
+       "\n",
+       "     ...  GSenergy_pa  HeatCapacityMolar  n_ws^third  HeatFusion  \\\n",
+       "0    ...    -3.331291             28.836        1.50       0.060   \n",
+       "1    ...     0.001135             20.786        0.00       0.000   \n",
+       "2    ...    -1.869887             24.860        0.98       3.000   \n",
+       "3    ...    -3.755039             16.443        1.67       7.895   \n",
+       "4    ...    -6.677698             11.087        1.75      50.200   \n",
+       "..   ...          ...                ...         ...         ...   \n",
+       "113  ...     0.000000              0.000        0.00       0.000   \n",
+       "114  ...     0.000000              0.000        0.00       0.000   \n",
+       "115  ...     0.000000              0.000        0.00       0.000   \n",
+       "116  ...     0.000000              0.000        0.00       0.000   \n",
+       "117  ...     0.000000              0.000        0.00       0.000   \n",
+       "\n",
+       "     ZungerPP-r_d  ZungerPP-r_pi  NpValence  Row  FusionEnthalpy  GSbandgap  \n",
+       "0             0.0          0.000        0.0  1.0           0.060      7.853  \n",
+       "1             0.0          0.000        0.0  1.0           0.000     18.098  \n",
+       "2             0.0          0.360        0.0  2.0           3.000      0.000  \n",
+       "3             0.0          0.200        0.0  2.0           7.895      0.000  \n",
+       "4             0.0          0.165        1.0  2.0          50.200      1.524  \n",
+       "..            ...            ...        ...  ...             ...        ...  \n",
+       "113           0.0          0.000        0.0  0.0           0.000      0.000  \n",
+       "114           0.0          0.000        0.0  0.0           0.000      0.000  \n",
+       "115           0.0          0.000        0.0  0.0           0.000      0.000  \n",
+       "116           0.0          0.000        0.0  0.0           0.000      0.000  \n",
+       "117           0.0          0.000        0.0  0.0           0.000      0.000  \n",
+       "\n",
+       "[118 rows x 70 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import mlband.features\n",
+    "mlband.features.pt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The selected properties when then will be one-hot-encoded to be used and the intial vector for each node."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['atomic number', 'atomic mass', 'group', 'period', 'MeltingT', 'MiracleRadius', 'CovalentRadius', 'FirstIonizationEnergy', 'GSestFCClatcnt', 'Electronegativity', 'Density', 'ZungerPP-r_s', 'DipolePolarizability', 'GSestBCClatcnt', 'LogThermalConductivity', 'NsValence', 'GSmagmom', 'Polarizability', 'NValence', 'IsAlkali', 'AtomicVolume', 'phi', 'Number', 'IsNonmetal', 'NfValence', 'HHIp', 'VdWRadius', 'HHIr', 'NpUnfilled', 'BulkModulus', 'ZungerPP-r_p', 'NsUnfilled', 'ZungerPP-r_sigma', 'ThermalConductivity', 'AtomicRadius', 'ShearModulus', 'IonizationEnergies', 'IsMetalloid', 'NfUnfilled', 'SecondIonizationEnergy', 'OxidationStates', 'IsDBlock', 'AtomicWeight', 'HeatVaporization', 'MendeleevNumber', 'GSvolume_pa', 'NUnfilled', 'IsMetal', 'ElectronAffinity', 'NdUnfilled', 'NdValence', 'MolarVolume', 'Column', 'ICSDVolume', 'IsFBlock', 'SpaceGroupNumber', 'HeatCapacityMass', 'AllenElectronegativity', 'BoilingT', 'GSenergy_pa', 'HeatCapacityMolar', 'n_ws^third', 'HeatFusion', 'ZungerPP-r_d', 'ZungerPP-r_pi', 'NpValence', 'Row', 'FusionEnthalpy', 'GSbandgap']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# List of available features\n",
+    "print(mlband.features.get_available_features())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n",
+      "{'0': 'group_0', '1': 'group_1', '2': 'group_2', '3': 'group_3', '4': 'group_4', '5': 'group_5', '6': 'group_6', '7': 'group_7', '8': 'group_8', '9': 'group_9', '10': 'group_10', '11': 'group_11', '12': 'group_12', '13': 'group_13', '14': 'group_14', '15': 'group_15', '16': 'group_16', '17': 'group_17', '18': 'group_18', '19': 'period_1', '20': 'period_2', '21': 'period_3', '22': 'period_4', '23': 'period_5', '24': 'period_6', '25': 'period_7'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Encoded features\n",
+    "f, col_names = mlband.features.one_hot_encode_elements(discrete_columns=['group', 'period'])\n",
+    "print(f['1']) # Features for hydrogen\n",
+    "print(col_names)\n",
+    "# The first 19 columns are the one-hot encoded group, the last 7 are the one-hot encoded period\n",
+    "# Group number 0 was considered for rare-earth elements "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+      "{'0': 'Electronegativity_bin_0', '1': 'Electronegativity_bin_1', '2': 'Electronegativity_bin_2', '3': 'Electronegativity_bin_3', '4': 'Electronegativity_bin_4', '5': 'Electronegativity_bin_5', '6': 'Electronegativity_bin_6', '7': 'Electronegativity_bin_7', '8': 'Electronegativity_bin_8', '9': 'Electronegativity_bin_9', '10': 'AtomicRadius_bin_0', '11': 'AtomicRadius_bin_1', '12': 'AtomicRadius_bin_2', '13': 'AtomicRadius_bin_3', '14': 'AtomicRadius_bin_4', '15': 'AtomicRadius_bin_5', '16': 'AtomicRadius_bin_6', '17': 'AtomicRadius_bin_7', '18': 'AtomicRadius_bin_8', '19': 'AtomicRadius_bin_9'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example 2:\n",
+    "f, col_names = mlband.features.one_hot_encode_elements(continuous_columns=['Electronegativity', 'AtomicRadius'])\n",
+    "print(f['1']) # Features for hydrogen\n",
+    "print(col_names)\n",
+    "# 10 discrete bins was considered for each continuous feature"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selected physical features:\n",
+    "\n",
+    "For the task of band gap predictions here are the list of selected properties for featurizing the elements: 1. Group number 2. Period Number 3. Electronegativity 4. Covalent radius 5. Valence electrons 6. First ionization energy 7. Electron affinity 8. Electron block (s,p,d,f) 9. Atomic volume\n",
+    "\n",
+    "I call these selected features original features and used it in the pretrained model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training a model\n",
+    "\n",
+    "The config class holds all the information regarding the model and data.\n",
+    "\n",
+    "~~~python\n",
+    "from mlband.imports import *\n",
+    "import mlband.data\n",
+    "import mlband.config\n",
+    "import mlband.model\n",
+    "import mlband.features\n",
+    "\n",
+    "data_set_path = 'data/mp-test/'\n",
+    "results_dir = 'results/test/'\n",
+    "\n",
+    "print('Preparing the config...', flush=True)\n",
+    "config = mlband.config.Config(\n",
+    "    data_path=data_set_path, \n",
+    "    workers=4, \n",
+    "    results_dir=results_dir,\n",
+    "    original_features=True, \n",
+    "    print_freq=100,\n",
+    "    )\n",
+    "\n",
+    "print('Creating and training the model...', flush=True)\n",
+    "mlband.model.train(config)\n",
+    "~~~"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predictions\n",
+    "\n",
+    "Here is the predictions on the test set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>True_Label</th>\n",
+       "      <th>Prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>mp-9937</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-0.052763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>mp-1172967</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.140506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>mp-982366</td>\n",
+       "      <td>2.5126</td>\n",
+       "      <td>1.816474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>mp-1175038</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.352745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mp-768127</td>\n",
+       "      <td>3.1253</td>\n",
+       "      <td>2.300649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3994</th>\n",
+       "      <td>mp-8399</td>\n",
+       "      <td>3.2859</td>\n",
+       "      <td>1.227919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3995</th>\n",
+       "      <td>mp-1112109</td>\n",
+       "      <td>2.2573</td>\n",
+       "      <td>1.406988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3996</th>\n",
+       "      <td>mp-1021162</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.039211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3997</th>\n",
+       "      <td>mp-1062676</td>\n",
+       "      <td>1.3146</td>\n",
+       "      <td>0.079352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3998</th>\n",
+       "      <td>mp-1187282</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-0.043594</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3999 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              ID  True_Label  Prediction\n",
+       "0        mp-9937      0.0000   -0.052763\n",
+       "1     mp-1172967      0.0000    0.140506\n",
+       "2      mp-982366      2.5126    1.816474\n",
+       "3     mp-1175038      0.0000    0.352745\n",
+       "4      mp-768127      3.1253    2.300649\n",
+       "...          ...         ...         ...\n",
+       "3994     mp-8399      3.2859    1.227919\n",
+       "3995  mp-1112109      2.2573    1.406988\n",
+       "3996  mp-1021162      0.0000    0.039211\n",
+       "3997  mp-1062676      1.3146    0.079352\n",
+       "3998  mp-1187282      0.0000   -0.043594\n",
+       "\n",
+       "[3999 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from mlband.imports import *\n",
+    "df = pd.read_csv('/Users/ali/GitHub/rewotes/adavaria/mlband/pretrained_model/predictions_test.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 354.331x218.989 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE = 0.449\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot the predicted vs target values parity plot\n",
+    "plt.scatter(df['True_Label'], df['Prediction'], alpha=0.25)\n",
+    "plt.xlabel('target band gap [eV]')\n",
+    "plt.ylabel('predicted band gap [eV]')\n",
+    "\n",
+    "x = np.linspace(-1, 7, 6)\n",
+    "y = x\n",
+    "plt.plot(x, y, '-r', label='parity')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# pring the mean absolute error\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "\n",
+    "mae = mean_absolute_error(df['True_Label'], df['Prediction'])\n",
+    "print(f'MAE = {mae:.3f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the pretrained model from the installed package\n",
+    "\n",
+    "After installing the package, you can perform predictions simply by calling the predict function and specifying the path to the cif files, which should be in the correct format and include the id_prop.csv file.\n",
+    "~~~python\n",
+    "import mlband\n",
+    "df = mlband.model.predict(data_path='/Users/ali/GitHub/rewotes/adavaria/data/mp-test/')\n",
+    "~~~\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explanation of different files:\n",
+    "\n",
+    "- `__init__.csv`: Includes the most important modules files that a user need to import.\n",
+    "- `imports.py`: Includes the most common imports that might be needed to avoid repeating them in different files.\n",
+    "- `conig.py` : Includes a class for configuration parameters of the model.\n",
+    "- `features.py`: Includes the functions to preprocess the elemental features and prepare the data for training in the right format.\n",
+    "- `model.py`: Includes the model class and the functions to train and predict the model.\n",
+    "- `main.py`: Includes the main function to collect data, train and predict the model.\n",
+    "- `mp.py`: Includes the functions to collect data from the Materials Project database.\n",
+    "- `plot_formatter.py`: It has the script to define the default plot format for the project.\n",
+    "- `utility.py`: It includes the general functions that are used in different files.\n",
+    "- `cgcnn`: This directory includes the files related to the Crystal Graph Convolutional Neural Network (CGCNN) model. All the functions in this directory are copied from the original CGCNN repository with some modifications to make it compatible with the project.\n",
+    "- `pretrained_model`: This directory includes the pre-trained model's weights and the predictions."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cgcnn",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adavaria/images/cgcnn.png b/adavaria/images/cgcnn.png
new file mode 100644
index 00000000..0404dbfa
Binary files /dev/null and b/adavaria/images/cgcnn.png differ
diff --git a/adavaria/mlband/__init__.py b/adavaria/mlband/__init__.py
new file mode 100644
index 00000000..8ccc7196
--- /dev/null
+++ b/adavaria/mlband/__init__.py
@@ -0,0 +1,4 @@
+import warnings
+warnings.filterwarnings("ignore")
+from . import model, data, config, main, plot_formatter, mp
+from . import utility as util
diff --git a/adavaria/mlband/atom_init.json b/adavaria/mlband/atom_init.json
new file mode 100644
index 00000000..b8699f5a
--- /dev/null
+++ b/adavaria/mlband/atom_init.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/adavaria/mlband/cgcnn/README.md b/adavaria/mlband/cgcnn/README.md
new file mode 100644
index 00000000..22758af3
--- /dev/null
+++ b/adavaria/mlband/cgcnn/README.md
@@ -0,0 +1,2 @@
+## CGCNN
+The codes on this directory are comming from [CGCNN](https://github.com/txie-93/cgcnn) repository with slight modifications.
\ No newline at end of file
diff --git a/adavaria/mlband/cgcnn/__init__.py b/adavaria/mlband/cgcnn/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/adavaria/mlband/cgcnn/data.py b/adavaria/mlband/cgcnn/data.py
new file mode 100644
index 00000000..bef2acd1
--- /dev/null
+++ b/adavaria/mlband/cgcnn/data.py
@@ -0,0 +1,352 @@
+from __future__ import print_function, division
+
+import csv
+import functools
+import json
+import os
+import random
+import warnings
+
+import numpy as np
+import torch
+from pymatgen.core.structure import Structure
+from torch.utils.data import Dataset, DataLoader
+from torch.utils.data.dataloader import default_collate
+from torch.utils.data.sampler import SubsetRandomSampler
+
+
+def get_train_val_test_loader(dataset, collate_fn=default_collate,
+                              batch_size=64, train_ratio=None,
+                              val_ratio=0.1, test_ratio=0.1, return_test=False,
+                              num_workers=1, pin_memory=False, **kwargs):
+    """
+    Utility function for dividing a dataset to train, val, test datasets.
+
+    !!! The dataset needs to be shuffled before using the function !!!
+
+    Parameters
+    ----------
+    dataset: torch.utils.data.Dataset
+      The full dataset to be divided.
+    collate_fn: torch.utils.data.DataLoader
+    batch_size: int
+    train_ratio: float
+    val_ratio: float
+    test_ratio: float
+    return_test: bool
+      Whether to return the test dataset loader. If False, the last test_size
+      data will be hidden.
+    num_workers: int
+    pin_memory: bool
+
+    Returns
+    -------
+    train_loader: torch.utils.data.DataLoader
+      DataLoader that random samples the training data.
+    val_loader: torch.utils.data.DataLoader
+      DataLoader that random samples the validation data.
+    (test_loader): torch.utils.data.DataLoader
+      DataLoader that random samples the test data, returns if
+        return_test=True.
+    """
+    total_size = len(dataset)
+    if kwargs['train_size'] is None:
+        if train_ratio is None:
+            assert val_ratio + test_ratio < 1
+            train_ratio = 1 - val_ratio - test_ratio
+            print(f'[Warning] train_ratio is None, using 1 - val_ratio - '
+                  f'test_ratio = {train_ratio} as training data.')
+        else:
+            assert train_ratio + val_ratio + test_ratio <= 1
+    indices = list(range(total_size))
+    if kwargs['train_size']:
+        train_size = kwargs['train_size']
+    else:
+        train_size = int(train_ratio * total_size)
+    if kwargs['test_size']:
+        test_size = kwargs['test_size']
+    else:
+        test_size = int(test_ratio * total_size)
+    if kwargs['val_size']:
+        valid_size = kwargs['val_size']
+    else:
+        valid_size = int(val_ratio * total_size)
+    train_sampler = SubsetRandomSampler(indices[:train_size])
+    val_sampler = SubsetRandomSampler(
+        indices[-(valid_size + test_size):-test_size])
+    if return_test:
+        test_sampler = SubsetRandomSampler(indices[-test_size:])
+    train_loader = DataLoader(dataset, batch_size=batch_size,
+                              sampler=train_sampler,
+                              num_workers=num_workers,
+                              collate_fn=collate_fn, pin_memory=pin_memory)
+    val_loader = DataLoader(dataset, batch_size=batch_size,
+                            sampler=val_sampler,
+                            num_workers=num_workers,
+                            collate_fn=collate_fn, pin_memory=pin_memory)
+    if return_test:
+        test_loader = DataLoader(dataset, batch_size=batch_size,
+                                 sampler=test_sampler,
+                                 num_workers=num_workers,
+                                 collate_fn=collate_fn, pin_memory=pin_memory)
+    if return_test:
+        return train_loader, val_loader, test_loader
+    else:
+        return train_loader, val_loader
+
+
+def collate_pool(dataset_list):
+    """
+    Collate a list of data and return a batch for predicting crystal
+    properties.
+
+    Parameters
+    ----------
+
+    dataset_list: list of tuples for each data point.
+      (atom_fea, nbr_fea, nbr_fea_idx, target)
+
+      atom_fea: torch.Tensor shape (n_i, atom_fea_len)
+      nbr_fea: torch.Tensor shape (n_i, M, nbr_fea_len)
+      nbr_fea_idx: torch.LongTensor shape (n_i, M)
+      target: torch.Tensor shape (1, )
+      cif_id: str or int
+
+    Returns
+    -------
+    N = sum(n_i); N0 = sum(i)
+
+    batch_atom_fea: torch.Tensor shape (N, orig_atom_fea_len)
+      Atom features from atom type
+    batch_nbr_fea: torch.Tensor shape (N, M, nbr_fea_len)
+      Bond features of each atom's M neighbors
+    batch_nbr_fea_idx: torch.LongTensor shape (N, M)
+      Indices of M neighbors of each atom
+    crystal_atom_idx: list of torch.LongTensor of length N0
+      Mapping from the crystal idx to atom idx
+    target: torch.Tensor shape (N, 1)
+      Target value for prediction
+    batch_cif_ids: list
+    """
+    batch_atom_fea, batch_nbr_fea, batch_nbr_fea_idx = [], [], []
+    crystal_atom_idx, batch_target = [], []
+    batch_cif_ids = []
+    base_idx = 0
+    for i, ((atom_fea, nbr_fea, nbr_fea_idx), target, cif_id)\
+            in enumerate(dataset_list):
+        n_i = atom_fea.shape[0]  # number of atoms for this crystal
+        batch_atom_fea.append(atom_fea)
+        batch_nbr_fea.append(nbr_fea)
+        batch_nbr_fea_idx.append(nbr_fea_idx+base_idx)
+        new_idx = torch.LongTensor(np.arange(n_i)+base_idx)
+        crystal_atom_idx.append(new_idx)
+        batch_target.append(target)
+        batch_cif_ids.append(cif_id)
+        base_idx += n_i
+    return (torch.cat(batch_atom_fea, dim=0),
+            torch.cat(batch_nbr_fea, dim=0),
+            torch.cat(batch_nbr_fea_idx, dim=0),
+            crystal_atom_idx),\
+        torch.stack(batch_target, dim=0),\
+        batch_cif_ids
+
+
+class GaussianDistance(object):
+    """
+    Expands the distance by Gaussian basis.
+
+    Unit: angstrom
+    """
+    def __init__(self, dmin, dmax, step, var=None):
+        """
+        Parameters
+        ----------
+
+        dmin: float
+          Minimum interatomic distance
+        dmax: float
+          Maximum interatomic distance
+        step: float
+          Step size for the Gaussian filter
+        """
+        assert dmin < dmax
+        assert dmax - dmin > step
+        self.filter = np.arange(dmin, dmax+step, step)
+        if var is None:
+            var = step
+        self.var = var
+
+    def expand(self, distances):
+        """
+        Apply Gaussian disntance filter to a numpy distance array
+
+        Parameters
+        ----------
+
+        distance: np.array shape n-d array
+          A distance matrix of any shape
+
+        Returns
+        -------
+        expanded_distance: shape (n+1)-d array
+          Expanded distance matrix with the last dimension of length
+          len(self.filter)
+        """
+        return np.exp(-(distances[..., np.newaxis] - self.filter)**2 /
+                      self.var**2)
+
+
+class AtomInitializer(object):
+    """
+    Base class for intializing the vector representation for atoms.
+
+    !!! Use one AtomInitializer per dataset !!!
+    """
+    def __init__(self, atom_types):
+        self.atom_types = set(atom_types)
+        self._embedding = {}
+
+    def get_atom_fea(self, atom_type):
+        assert atom_type in self.atom_types
+        return self._embedding[atom_type]
+
+    def load_state_dict(self, state_dict):
+        self._embedding = state_dict
+        self.atom_types = set(self._embedding.keys())
+        self._decodedict = {idx: atom_type for atom_type, idx in
+                            self._embedding.items()}
+
+    def state_dict(self):
+        return self._embedding
+
+    def decode(self, idx):
+        if not hasattr(self, '_decodedict'):
+            self._decodedict = {idx: atom_type for atom_type, idx in
+                                self._embedding.items()}
+        return self._decodedict[idx]
+
+
+class AtomCustomJSONInitializer(AtomInitializer):
+    """
+    Initialize atom feature vectors using a JSON file, which is a python
+    dictionary mapping from element number to a list representing the
+    feature vector of the element.
+
+    Parameters
+    ----------
+
+    elem_embedding_file: str
+        The path to the .json file
+    """
+    def __init__(self, elem_embedding_file):
+        with open(elem_embedding_file) as f:
+            elem_embedding = json.load(f)
+        elem_embedding = {int(key): value for key, value
+                          in elem_embedding.items()}
+        atom_types = set(elem_embedding.keys())
+        super(AtomCustomJSONInitializer, self).__init__(atom_types)
+        for key, value in elem_embedding.items():
+            self._embedding[key] = np.array(value, dtype=float)
+
+
+class CIFData(Dataset):
+    """
+    The CIFData dataset is a wrapper for a dataset where the crystal structures
+    are stored in the form of CIF files. The dataset should have the following
+    directory structure:
+
+    root_dir
+    ├── id_prop.csv
+    ├── atom_init.json
+    ├── id0.cif
+    ├── id1.cif
+    ├── ...
+
+    id_prop.csv: a CSV file with two columns. The first column recodes a
+    unique ID for each crystal, and the second column recodes the value of
+    target property.
+
+    atom_init.json: a JSON file that stores the initialization vector for each
+    element.
+
+    ID.cif: a CIF file that recodes the crystal structure, where ID is the
+    unique ID for the crystal.
+
+    Parameters
+    ----------
+
+    root_dir: str
+        The path to the root directory of the dataset
+    max_num_nbr: int
+        The maximum number of neighbors while constructing the crystal graph
+    radius: float
+        The cutoff radius for searching neighbors
+    dmin: float
+        The minimum distance for constructing GaussianDistance
+    step: float
+        The step size for constructing GaussianDistance
+    random_seed: int
+        Random seed for shuffling the dataset
+
+    Returns
+    -------
+
+    atom_fea: torch.Tensor shape (n_i, atom_fea_len)
+    nbr_fea: torch.Tensor shape (n_i, M, nbr_fea_len)
+    nbr_fea_idx: torch.LongTensor shape (n_i, M)
+    target: torch.Tensor shape (1, )
+    cif_id: str or int
+    """
+    def __init__(self, root_dir, max_num_nbr=12, radius=8, dmin=0, step=0.2,
+                 random_seed=123):
+        self.root_dir = str(root_dir)
+        self.max_num_nbr, self.radius = max_num_nbr, radius
+        assert os.path.exists(root_dir), f'{root_dir} does not exist!'
+        id_prop_file = os.path.join(self.root_dir, 'id_prop.csv')
+        assert os.path.exists(id_prop_file), f'id_prop.csv does not exist! ({id_prop_file})'
+        with open(id_prop_file) as f:
+            reader = csv.reader(f)
+            self.id_prop_data = [row for row in reader]
+        random.seed(random_seed)
+        random.shuffle(self.id_prop_data)
+        atom_init_file = os.path.join(self.root_dir, 'atom_init.json')
+        assert os.path.exists(atom_init_file), 'atom_init.json does not exist!'
+        self.ari = AtomCustomJSONInitializer(atom_init_file)
+        self.gdf = GaussianDistance(dmin=dmin, dmax=self.radius, step=step)
+
+    def __len__(self):
+        return len(self.id_prop_data)
+
+    @functools.lru_cache(maxsize=None)  # Cache loaded structures
+    def __getitem__(self, idx):
+        cif_id, target = self.id_prop_data[idx]
+        crystal = Structure.from_file(os.path.join(self.root_dir,
+                                                   cif_id+'.cif'))
+        atom_fea = np.vstack([self.ari.get_atom_fea(crystal[i].specie.number)
+                              for i in range(len(crystal))])
+        atom_fea = torch.Tensor(atom_fea)
+        all_nbrs = crystal.get_all_neighbors(self.radius, include_index=True)
+        all_nbrs = [sorted(nbrs, key=lambda x: x[1]) for nbrs in all_nbrs]
+        nbr_fea_idx, nbr_fea = [], []
+        for nbr in all_nbrs:
+            if len(nbr) < self.max_num_nbr:
+                warnings.warn('{} not find enough neighbors to build graph. '
+                              'If it happens frequently, consider increase '
+                              'radius.'.format(cif_id))
+                nbr_fea_idx.append(list(map(lambda x: x[2], nbr)) +
+                                   [0] * (self.max_num_nbr - len(nbr)))
+                nbr_fea.append(list(map(lambda x: x[1], nbr)) +
+                               [self.radius + 1.] * (self.max_num_nbr -
+                                                     len(nbr)))
+            else:
+                nbr_fea_idx.append(list(map(lambda x: x[2],
+                                            nbr[:self.max_num_nbr])))
+                nbr_fea.append(list(map(lambda x: x[1],
+                                        nbr[:self.max_num_nbr])))
+        nbr_fea_idx, nbr_fea = np.array(nbr_fea_idx), np.array(nbr_fea)
+        nbr_fea = self.gdf.expand(nbr_fea)
+        atom_fea = torch.Tensor(atom_fea)
+        nbr_fea = torch.Tensor(nbr_fea)
+        nbr_fea_idx = torch.LongTensor(nbr_fea_idx)
+        target = torch.Tensor([float(target)])
+        return (atom_fea, nbr_fea, nbr_fea_idx), target, cif_id
diff --git a/adavaria/mlband/cgcnn/model.py b/adavaria/mlband/cgcnn/model.py
new file mode 100644
index 00000000..de739532
--- /dev/null
+++ b/adavaria/mlband/cgcnn/model.py
@@ -0,0 +1,187 @@
+from __future__ import print_function, division
+
+import torch
+import torch.nn as nn
+
+
+class ConvLayer(nn.Module):
+    """
+    Convolutional operation on graphs
+    """
+    def __init__(self, atom_fea_len, nbr_fea_len):
+        """
+        Initialize ConvLayer.
+
+        Parameters
+        ----------
+
+        atom_fea_len: int
+          Number of atom hidden features.
+        nbr_fea_len: int
+          Number of bond features.
+        """
+        super(ConvLayer, self).__init__()
+        self.atom_fea_len = atom_fea_len
+        self.nbr_fea_len = nbr_fea_len
+        self.fc_full = nn.Linear(2*self.atom_fea_len+self.nbr_fea_len,
+                                 2*self.atom_fea_len)
+        self.sigmoid = nn.Sigmoid()
+        self.softplus1 = nn.Softplus()
+        self.bn1 = nn.BatchNorm1d(2*self.atom_fea_len)
+        self.bn2 = nn.BatchNorm1d(self.atom_fea_len)
+        self.softplus2 = nn.Softplus()
+
+    def forward(self, atom_in_fea, nbr_fea, nbr_fea_idx):
+        """
+        Forward pass
+
+        N: Total number of atoms in the batch
+        M: Max number of neighbors
+
+        Parameters
+        ----------
+
+        atom_in_fea: Variable(torch.Tensor) shape (N, atom_fea_len)
+          Atom hidden features before convolution
+        nbr_fea: Variable(torch.Tensor) shape (N, M, nbr_fea_len)
+          Bond features of each atom's M neighbors
+        nbr_fea_idx: torch.LongTensor shape (N, M)
+          Indices of M neighbors of each atom
+
+        Returns
+        -------
+
+        atom_out_fea: nn.Variable shape (N, atom_fea_len)
+          Atom hidden features after convolution
+
+        """
+        # TODO will there be problems with the index zero padding?
+        N, M = nbr_fea_idx.shape
+        # convolution
+        atom_nbr_fea = atom_in_fea[nbr_fea_idx, :]
+        total_nbr_fea = torch.cat(
+            [atom_in_fea.unsqueeze(1).expand(N, M, self.atom_fea_len),
+             atom_nbr_fea, nbr_fea], dim=2)
+        total_gated_fea = self.fc_full(total_nbr_fea)
+        total_gated_fea = self.bn1(total_gated_fea.view(
+            -1, self.atom_fea_len*2)).view(N, M, self.atom_fea_len*2)
+        nbr_filter, nbr_core = total_gated_fea.chunk(2, dim=2)
+        nbr_filter = self.sigmoid(nbr_filter)
+        nbr_core = self.softplus1(nbr_core)
+        nbr_sumed = torch.sum(nbr_filter * nbr_core, dim=1)
+        nbr_sumed = self.bn2(nbr_sumed)
+        out = self.softplus2(atom_in_fea + nbr_sumed)
+        return out
+
+
+class CrystalGraphConvNet(nn.Module):
+    """
+    Create a crystal graph convolutional neural network for predicting total
+    material properties.
+    """
+    def __init__(self, orig_atom_fea_len, nbr_fea_len,
+                 atom_fea_len=64, n_conv=3, h_fea_len=128, n_h=1,
+                 classification=False):
+        """
+        Initialize CrystalGraphConvNet.
+
+        Parameters
+        ----------
+
+        orig_atom_fea_len: int
+          Number of atom features in the input.
+        nbr_fea_len: int
+          Number of bond features.
+        atom_fea_len: int
+          Number of hidden atom features in the convolutional layers
+        n_conv: int
+          Number of convolutional layers
+        h_fea_len: int
+          Number of hidden features after pooling
+        n_h: int
+          Number of hidden layers after pooling
+        """
+        super(CrystalGraphConvNet, self).__init__()
+        self.classification = classification
+        self.embedding = nn.Linear(orig_atom_fea_len, atom_fea_len)
+        self.convs = nn.ModuleList([ConvLayer(atom_fea_len=atom_fea_len,
+                                    nbr_fea_len=nbr_fea_len)
+                                    for _ in range(n_conv)])
+        self.conv_to_fc = nn.Linear(atom_fea_len, h_fea_len)
+        self.conv_to_fc_softplus = nn.Softplus()
+        if n_h > 1:
+            self.fcs = nn.ModuleList([nn.Linear(h_fea_len, h_fea_len)
+                                      for _ in range(n_h-1)])
+            self.softpluses = nn.ModuleList([nn.Softplus()
+                                             for _ in range(n_h-1)])
+        if self.classification:
+            self.fc_out = nn.Linear(h_fea_len, 2)
+        else:
+            self.fc_out = nn.Linear(h_fea_len, 1)
+        if self.classification:
+            self.logsoftmax = nn.LogSoftmax(dim=1)
+            self.dropout = nn.Dropout()
+
+    def forward(self, atom_fea, nbr_fea, nbr_fea_idx, crystal_atom_idx):
+        """
+        Forward pass
+
+        N: Total number of atoms in the batch
+        M: Max number of neighbors
+        N0: Total number of crystals in the batch
+
+        Parameters
+        ----------
+
+        atom_fea: Variable(torch.Tensor) shape (N, orig_atom_fea_len)
+          Atom features from atom type
+        nbr_fea: Variable(torch.Tensor) shape (N, M, nbr_fea_len)
+          Bond features of each atom's M neighbors
+        nbr_fea_idx: torch.LongTensor shape (N, M)
+          Indices of M neighbors of each atom
+        crystal_atom_idx: list of torch.LongTensor of length N0
+          Mapping from the crystal idx to atom idx
+
+        Returns
+        -------
+
+        prediction: nn.Variable shape (N, )
+          Atom hidden features after convolution
+
+        """
+        atom_fea = self.embedding(atom_fea)
+        for conv_func in self.convs:
+            atom_fea = conv_func(atom_fea, nbr_fea, nbr_fea_idx)
+        crys_fea = self.pooling(atom_fea, crystal_atom_idx)
+        crys_fea = self.conv_to_fc(self.conv_to_fc_softplus(crys_fea))
+        crys_fea = self.conv_to_fc_softplus(crys_fea)
+        if self.classification:
+            crys_fea = self.dropout(crys_fea)
+        if hasattr(self, 'fcs') and hasattr(self, 'softpluses'):
+            for fc, softplus in zip(self.fcs, self.softpluses):
+                crys_fea = softplus(fc(crys_fea))
+        out = self.fc_out(crys_fea)
+        if self.classification:
+            out = self.logsoftmax(out)
+        return out
+
+    def pooling(self, atom_fea, crystal_atom_idx):
+        """
+        Pooling the atom features to crystal features
+
+        N: Total number of atoms in the batch
+        N0: Total number of crystals in the batch
+
+        Parameters
+        ----------
+
+        atom_fea: Variable(torch.Tensor) shape (N, atom_fea_len)
+          Atom feature vectors of the batch
+        crystal_atom_idx: list of torch.LongTensor of length N0
+          Mapping from the crystal idx to atom idx
+        """
+        assert sum([len(idx_map) for idx_map in crystal_atom_idx]) ==\
+            atom_fea.data.shape[0]
+        summed_fea = [torch.mean(atom_fea[idx_map], dim=0, keepdim=True)
+                      for idx_map in crystal_atom_idx]
+        return torch.cat(summed_fea, dim=0)
diff --git a/adavaria/mlband/cgcnn/train.py b/adavaria/mlband/cgcnn/train.py
new file mode 100644
index 00000000..9ff28fff
--- /dev/null
+++ b/adavaria/mlband/cgcnn/train.py
@@ -0,0 +1,241 @@
+from .utility import AverageMeter, mae, class_eval
+from torch.autograd import Variable
+import time
+import torch
+
+
+def train_epoch(args, train_loader, model, criterion, optimizer, epoch, normalizer):
+    batch_time = AverageMeter()
+    data_time = AverageMeter()
+    losses = AverageMeter()
+    if args.task == 'regression':
+        mae_errors = AverageMeter()
+    else:
+        accuracies = AverageMeter()
+        precisions = AverageMeter()
+        recalls = AverageMeter()
+        fscores = AverageMeter()
+        auc_scores = AverageMeter()
+
+    # switch to train mode
+    model.train()
+
+    end = time.time()
+    for i, (input, target, _) in enumerate(train_loader):
+        # measure data loading time
+        data_time.update(time.time() - end)
+
+        if args.cuda:
+            input_var = (Variable(input[0].cuda(non_blocking=True)),
+                         Variable(input[1].cuda(non_blocking=True)),
+                         input[2].cuda(non_blocking=True),
+                         [crys_idx.cuda(non_blocking=True) for crys_idx in input[3]])
+        else:
+            input_var = (Variable(input[0]),
+                         Variable(input[1]),
+                         input[2],
+                         input[3])
+        # normalize target
+        if args.task == 'regression':
+            target_normed = normalizer.norm(target)
+        else:
+            target_normed = target.view(-1).long()
+        if args.cuda:
+            target_var = Variable(target_normed.cuda(non_blocking=True))
+        else:
+            target_var = Variable(target_normed)
+
+        # compute output
+        output = model(*input_var)
+        loss = criterion(output, target_var)
+
+        # measure accuracy and record loss
+        if args.task == 'regression':
+            mae_error = mae(normalizer.denorm(output.data.cpu()), target)
+            losses.update(loss.data.cpu(), target.size(0))
+            mae_errors.update(mae_error, target.size(0))
+        else:
+            accuracy, precision, recall, fscore, auc_score = \
+                class_eval(output.data.cpu(), target)
+            losses.update(loss.data.cpu().item(), target.size(0))
+            accuracies.update(accuracy, target.size(0))
+            precisions.update(precision, target.size(0))
+            recalls.update(recall, target.size(0))
+            fscores.update(fscore, target.size(0))
+            auc_scores.update(auc_score, target.size(0))
+
+        # compute gradient and do SGD step
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # measure elapsed time
+        batch_time.update(time.time() - end)
+        end = time.time()
+
+        if i % args.print_freq == 0:
+            if args.task == 'regression':
+                print('Epoch: [{0}][{1}/{2}]\t'
+                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                      'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
+                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                      'MAE {mae_errors.val:.3f} ({mae_errors.avg:.3f})'.format(
+                    epoch, i, len(train_loader), batch_time=batch_time,
+                    data_time=data_time, loss=losses, mae_errors=mae_errors)
+                )
+            else:
+                print('Epoch: [{0}][{1}/{2}]\t'
+                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                      'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
+                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                      'Accu {accu.val:.3f} ({accu.avg:.3f})\t'
+                      'Precision {prec.val:.3f} ({prec.avg:.3f})\t'
+                      'Recall {recall.val:.3f} ({recall.avg:.3f})\t'
+                      'F1 {f1.val:.3f} ({f1.avg:.3f})\t'
+                      'AUC {auc.val:.3f} ({auc.avg:.3f})'.format(
+                    epoch, i, len(train_loader), batch_time=batch_time,
+                    data_time=data_time, loss=losses, accu=accuracies,
+                    prec=precisions, recall=recalls, f1=fscores,
+                    auc=auc_scores)
+                )
+
+
+def validate(args, val_loader, model, criterion, normalizer, test=False):
+    batch_time = AverageMeter()
+    losses = AverageMeter()
+    if args.task == 'regression':
+        mae_errors = AverageMeter()
+    else:
+        accuracies = AverageMeter()
+        precisions = AverageMeter()
+        recalls = AverageMeter()
+        fscores = AverageMeter()
+        auc_scores = AverageMeter()
+    if test:
+        test_targets = []
+        test_preds = []
+        test_cif_ids = []
+
+    # switch to evaluate mode
+    model.eval()
+
+    end = time.time()
+    for i, (input, target, batch_cif_ids) in enumerate(val_loader):
+        if args.cuda:
+            with torch.no_grad():
+                input_var = (Variable(input[0].cuda(non_blocking=True)),
+                             Variable(input[1].cuda(non_blocking=True)),
+                             input[2].cuda(non_blocking=True),
+                             [crys_idx.cuda(non_blocking=True) for crys_idx in input[3]])
+        else:
+            with torch.no_grad():
+                input_var = (Variable(input[0]),
+                             Variable(input[1]),
+                             input[2],
+                             input[3])
+        if args.task == 'regression':
+            target_normed = normalizer.norm(target)
+        else:
+            target_normed = target.view(-1).long()
+        if args.cuda:
+            with torch.no_grad():
+                target_var = Variable(target_normed.cuda(non_blocking=True))
+        else:
+            with torch.no_grad():
+                target_var = Variable(target_normed)
+
+        # compute output
+        output = model(*input_var)
+        loss = criterion(output, target_var)
+
+        # measure accuracy and record loss
+        if args.task == 'regression':
+            mae_error = mae(normalizer.denorm(output.data.cpu()), target)
+            losses.update(loss.data.cpu().item(), target.size(0))
+            mae_errors.update(mae_error, target.size(0))
+            if test:
+                test_pred = normalizer.denorm(output.data.cpu())
+                test_target = target
+                test_preds += test_pred.view(-1).tolist()
+                test_targets += test_target.view(-1).tolist()
+                test_cif_ids += batch_cif_ids
+        else:
+            accuracy, precision, recall, fscore, auc_score = \
+                class_eval(output.data.cpu(), target)
+            losses.update(loss.data.cpu().item(), target.size(0))
+            accuracies.update(accuracy, target.size(0))
+            precisions.update(precision, target.size(0))
+            recalls.update(recall, target.size(0))
+            fscores.update(fscore, target.size(0))
+            auc_scores.update(auc_score, target.size(0))
+            if test:
+                test_pred = torch.exp(output.data.cpu())
+                test_target = target
+                assert test_pred.shape[1] == 2
+                test_preds += test_pred[:, 1].tolist()
+                test_targets += test_target.view(-1).tolist()
+                test_cif_ids += batch_cif_ids
+
+        # measure elapsed time
+        batch_time.update(time.time() - end)
+        end = time.time()
+
+        if i % args.print_freq == 0:
+            if args.task == 'regression':
+                print('Test: [{0}/{1}]\t'
+                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                      'MAE {mae_errors.val:.3f} ({mae_errors.avg:.3f})'.format(
+                    i, len(val_loader), batch_time=batch_time, loss=losses,
+                    mae_errors=mae_errors))
+            else:
+                print('Test: [{0}/{1}]\t'
+                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                      'Accu {accu.val:.3f} ({accu.avg:.3f})\t'
+                      'Precision {prec.val:.3f} ({prec.avg:.3f})\t'
+                      'Recall {recall.val:.3f} ({recall.avg:.3f})\t'
+                      'F1 {f1.val:.3f} ({f1.avg:.3f})\t'
+                      'AUC {auc.val:.3f} ({auc.avg:.3f})'.format(
+                    i, len(val_loader), batch_time=batch_time, loss=losses,
+                    accu=accuracies, prec=precisions, recall=recalls,
+                    f1=fscores, auc=auc_scores))
+
+    if test:
+        star_label = '**'
+        # import csv
+        # with open('test_results.csv', 'w') as f:
+        #     writer = csv.writer(f)
+        #     for cif_id, target, pred in zip(test_cif_ids, test_targets,
+        #                                     test_preds):
+        #         writer.writerow((cif_id, target, pred))
+    else:
+        star_label = '*'
+    if args.task == 'regression':
+        print(' {star} MAE {mae_errors.avg:.3f}'.format(star=star_label,
+                                                        mae_errors=mae_errors))
+        return mae_errors.avg
+    else:
+        print(' {star} AUC {auc.avg:.3f}'.format(star=star_label,
+                                                 auc=auc_scores))
+        return auc_scores.avg
+
+
+def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', cd=None):
+    """Saves checkpoint to disk. Optionally changes directory before saving."""
+    import os
+    import shutil
+    original_dir = os.getcwd()  # Save the current working directory
+    
+    if cd:
+        os.chdir(cd)  # Change to the new directory
+
+    try:
+        # Save the state
+        torch.save(state, filename)
+        
+        if is_best:
+            shutil.copyfile(filename, 'model_best.pth.tar')
+    finally:
+        os.chdir(original_dir)  # Change back to the original directory
+
diff --git a/adavaria/mlband/cgcnn/utility.py b/adavaria/mlband/cgcnn/utility.py
new file mode 100644
index 00000000..14e0a5e5
--- /dev/null
+++ b/adavaria/mlband/cgcnn/utility.py
@@ -0,0 +1,79 @@
+import torch
+import numpy as np
+from sklearn import metrics
+
+
+
+class Normalizer(object):
+    """
+    Normalize a Tensor and restore it later. 
+    Taken from: https://github.com/txie-93/cgcnn
+    """
+
+    def __init__(self, tensor=torch.zeros(3)):
+        """tensor is taken as a sample to calculate the mean and std"""
+        self.mean = torch.mean(tensor)
+        self.std = torch.std(tensor)
+
+    def norm(self, tensor):
+        """Normalize a tensor"""
+        return (tensor - self.mean) / self.std
+
+    def denorm(self, normed_tensor):
+        """Denormalize a normalized tensor"""
+        return normed_tensor * self.std + self.mean
+
+    def state_dict(self):
+        """Returns a dictionary containing a whole state of the module."""
+        return {'mean': self.mean,
+                'std': self.std}
+
+    def load_state_dict(self, state_dict):
+        """Loads a state dict."""
+        self.mean = state_dict['mean']
+        self.std = state_dict['std']
+        return self
+
+
+class AverageMeter(object):
+    """
+    Computes and stores the average and current value.
+    Taken from: https://github.com/txie-93/cgcnn
+    """
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+def class_eval(prediction, target):
+    prediction = np.exp(prediction.numpy())
+    target = target.numpy()
+    pred_label = np.argmax(prediction, axis=1)
+    target_label = np.squeeze(target)
+    if not target_label.shape:
+        target_label = np.asarray([target_label])
+    if prediction.shape[1] == 2:
+        precision, recall, fscore, _ = metrics.precision_recall_fscore_support(
+            target_label, pred_label, average='binary')
+        auc_score = metrics.roc_auc_score(target_label, prediction[:, 1])
+        accuracy = metrics.accuracy_score(target_label, pred_label)
+    else:
+        raise NotImplementedError
+    return accuracy, precision, recall, fscore, auc_score
+
+def mae(prediction, target):
+    return torch.mean(torch.abs(target - prediction))
+
+def mse(prediction, target):
+    return torch.mean((target - prediction)**2)
\ No newline at end of file
diff --git a/adavaria/mlband/config.py b/adavaria/mlband/config.py
new file mode 100644
index 00000000..368112df
--- /dev/null
+++ b/adavaria/mlband/config.py
@@ -0,0 +1,134 @@
+import torch
+# import os
+from pathlib import Path
+import warnings
+
+
+class Config:
+    def __init__(self, data_path, disable_cuda=True, workers=0, epochs=30,
+                 batch_size=64, learning_rate=0.01, lr_milestones=None, momentum=0.9,
+                 weight_decay=0, print_freq=10, resume='', train_ratio=None,
+                 train_size=None, val_ratio=0.1, val_size=None, test_ratio=0.1,
+                 test_size=None, optim='Adam', atom_fea_len=64, h_fea_len=128,
+                 n_conv=3, n_h=1, task='regression',
+                 results_dir='./results',
+                 original_features=False,
+                 discrete_columns=None,
+                 continuous_columns=None,
+                 start_epoch=0,
+                 ):
+        """
+        data_path: str (path to data), e.g. './data/mp/'
+        disable_cuda: bool (disable cuda), default: True
+        workers: int (number of workers for data loader), default: 0
+        epochs: int (number of epochs to train), default: 30
+        batch_size: int (batch size for training and prediction), default: 128
+        learning_rate: float (initial learning rate), default: 0.01
+        lr_milestones: list of int (epochs to decrease learning rate), default: [100]
+        momentum: float (SGD momentum), default: 0.9
+        weight_decay: float (weight decay), default: 0
+        print_freq: int (frequency of printing training status per batch), default: 10
+        resume: str (path to checkpoint)
+        train_ratio: float (ratio of training set), default: None
+        train_size: int (size of training set), default: None
+        val_ratio: float (ratio of validation set), default: 0.1
+        val_size: int (size of validation set), default: None
+        test_ratio: float (ratio of test set), default: 0.1
+        test_size: int (size of test set), default: None
+        optim: str (optimizer, SGD or Adam), default: Adam
+        atom_fea_len: int (length of atom feature vector), default: 64
+        h_fea_len: int (length of hidden vector), default: 128
+        n_conv: int (number of convolutional layers), default: 3
+        n_h: int (number of hidden layers), default: 1
+        task: str (regression or classification), default: regression
+        start_epoch: int (start epoch for training), default: 0
+
+        results_dir: str (path to save results), default: './results'
+        """
+        if lr_milestones is None:
+            lr_milestones = [100]
+
+        self.data_path = data_path
+        self.disable_cuda = disable_cuda
+        self.cuda = not self.disable_cuda and torch.cuda.is_available()
+        self.workers = workers
+        self.epochs = epochs
+        self.batch_size = batch_size
+        self.learning_rate = learning_rate
+        self.lr_milestones = lr_milestones
+        self.momentum = momentum
+        self.weight_decay = weight_decay
+        self.print_freq = print_freq
+        self.resume = resume
+        self.train_ratio = train_ratio if train_ratio is not None else 1 - val_ratio - test_ratio
+        self.train_size = train_size
+        self.val_ratio = val_ratio
+        self.val_size = val_size
+        self.test_ratio = test_ratio
+        self.test_size = test_size
+        self.optim = optim
+        self.atom_fea_len = atom_fea_len
+        self.h_fea_len = h_fea_len
+        self.n_conv = n_conv
+        self.n_h = n_h
+        self.task = task
+        self.results_dir = Path(results_dir)
+        # Make sure the results_dir exists
+        self.results_dir.mkdir(parents=True, exist_ok=True)
+        self.original_features = original_features
+        self.discrete_columns = discrete_columns
+        self.continuous_columns = continuous_columns
+        self.start_epoch = start_epoch
+
+
+    def generate_args(self, absolute_path=True):
+        # args = [self.data_path]
+        args = [Path(self.data_path).absolute().__str__() if
+                absolute_path else self.data_path.__str__()]
+        if self.disable_cuda:
+            args.append('--disable-cuda')
+        args.extend(['--workers', str(self.workers),
+                     '--epochs', str(self.epochs),
+                     '--batch-size', str(self.batch_size),
+                     '--lr', str(self.learning_rate),
+                     '--lr-milestones'] + [str(milestone) for milestone in self.lr_milestones])
+        args.extend(['--momentum', str(self.momentum),
+                     '--weight-decay', str(self.weight_decay),
+                     '--print-freq', str(self.print_freq),
+                     '--resume', self.resume,
+                     '--optim', self.optim,
+                     '--atom-fea-len', str(self.atom_fea_len),
+                     '--h-fea-len', str(self.h_fea_len),
+                     '--n-conv', str(self.n_conv),
+                     '--n-h', str(self.n_h),
+                     '--task', self.task])
+
+        if self.train_ratio is not None:
+            args.extend(['--train-ratio', str(self.train_ratio)])
+        elif self.train_size is not None:
+            args.extend(['--train-size', str(self.train_size)])
+
+        if self.val_ratio is not None:
+            args.extend(['--val-ratio', str(self.val_ratio)])
+        elif self.val_size is not None:
+            args.extend(['--val-size', str(self.val_size)])
+
+        if self.test_ratio is not None:
+            args.extend(['--test-ratio', str(self.test_ratio)])
+        elif self.test_size is not None:
+            args.extend(['--test-size', str(self.test_size)])
+
+        return args
+    
+
+    # def load_default_model(self):
+    #     path = Path(__file__).parent / 'pretrained_model'
+    #     self.results_dir = path
+
+
+
+if __name__ == '__main__':
+    print('Testing Config...')
+    args_t = Config('./data/mp/')
+    print(args_t.generate_args())
+    print('Done!')
diff --git a/adavaria/mlband/data.py b/adavaria/mlband/data.py
new file mode 100644
index 00000000..52ad14de
--- /dev/null
+++ b/adavaria/mlband/data.py
@@ -0,0 +1,85 @@
+import numpy as np
+
+import mlband.mp as MP
+from pathlib import Path
+import pandas as pd
+
+
+# elem_embedding_file='cgcnn/data/sample-regression/atom_init.json'
+
+def get_list_of_materials(filter_data=True, num_chunks=1):
+    fields = ['material_id', 'composition', 'band_gap', 'structure']
+    data = MP.mpr.materials.summary.search(
+        # chemsys="Si-O", 
+        fields=fields,
+        chunk_size=1000,
+        num_chunks=num_chunks,
+    )
+
+    df = pd.DataFrame({
+        'material_id': [doc.material_id.__str__() for doc in data],
+        'band_gap': [doc.band_gap for doc in data],
+        'structure': [doc.structure for doc in data],
+    })
+    # df = df[fields]
+
+    if filter_data:
+        # Checking the missing values
+        ind = ~(df['band_gap'] >= 0)
+        print('Number of missing values: ', np.sum(ind))
+        # Drop the missing values
+        df = df.loc[~ind, :]
+        # Reset the index
+        df.reset_index(drop=True, inplace=True)
+
+    return df, data
+
+
+def create_dataset(df, path='data/cif_files'):
+    path = Path(path)
+    path.mkdir(parents=True, exist_ok=True)
+    for _, row in df.iterrows():
+        structure = row['structure']
+        material_id = row['material_id']
+        # composition = row['composition']
+        # band_gap = row['band_gap']
+        filename = path / f'{material_id}.cif'
+        writer = MP.CifWriter(structure)
+        writer.write_file(filename)
+        # print(f'Crystal structure {i+1} of {len(df)} written to {filename}.')
+
+    df[['material_id', 'band_gap']].to_csv(path / 'id_prop.csv', index=False, header=False)
+    # import shutil
+    # shutil.copy(elem_embedding_file, path / 'atom_init.json')
+
+
+def get_data_loaders(args):
+    from .cgcnn.data import CIFData, collate_pool, get_train_val_test_loader
+    dataset = CIFData(args.data_path)
+    collate_fn = collate_pool
+    train_loader, val_loader, test_loader = get_train_val_test_loader(
+        dataset=dataset,
+        collate_fn=collate_fn,
+        batch_size=args.batch_size,
+        train_ratio=args.train_ratio,
+        num_workers=args.workers,
+        val_ratio=args.val_ratio,
+        test_ratio=args.test_ratio,
+        pin_memory=args.cuda,
+        train_size=args.train_size,
+        val_size=args.val_size,
+        test_size=args.test_size,
+        return_test=True)
+    return train_loader, val_loader, test_loader
+
+
+def get_data_loader(args):
+    from .cgcnn.data import CIFData, collate_pool, DataLoader
+    dataset = CIFData(args.data_path)
+    collate_fn = collate_pool
+
+    data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True,
+                            num_workers=args.workers, collate_fn=collate_fn,
+                            pin_memory=args.cuda)
+    
+    return data_loader
\ No newline at end of file
diff --git a/adavaria/mlband/features.py b/adavaria/mlband/features.py
new file mode 100644
index 00000000..72e899d2
--- /dev/null
+++ b/adavaria/mlband/features.py
@@ -0,0 +1,83 @@
+from .imports import *
+from . import magpie
+
+pt = magpie.get_magpie_data()
+
+
+def one_hot_encode_elements(config=None, discrete_columns=None, continuous_columns=None, n_bins=10, filename=None):
+    """
+    One-hot encodes the elements. To get the list of available features, use the get_available_features() function.
+
+    Parameters
+    ----------
+    config : Config
+        The configuration object.
+    discrete_columns : list of str
+        A list of column names that contain discrete values.
+    continuous_columns : list of str
+        A list of column names that contain continuous values.
+    n_bins : int
+        The number of bins to use when binning continuous values.
+    filename : str
+        The name of the file to save the one-hot encoding dictionary to.
+    """
+    df = pt
+    if config is not None:
+        if config.original_features:
+            import shutil
+            # shutil.copy('mlband/atom_init.json', config.data_path)
+            json_file = Path(Path(__file__).parent, 'atom_init.json')
+            shutil.copy(json_file, config.data_path)
+            # read the json file
+            import json
+            with open(json_file) as f:
+                data = json.load(f)
+            return data, 'original_features'
+        filename = Path(config.data_path) / 'atom_init.json'
+        discrete_columns = config.discrete_columns
+        continuous_columns = config.continuous_columns
+
+    if discrete_columns is None:
+        discrete_columns = []
+    if continuous_columns is None:
+        continuous_columns = []
+
+    encoded_df = pd.DataFrame()
+
+    # One-hot encode discrete variables
+    for col in discrete_columns:
+        encoded_df = pd.concat([encoded_df, pd.get_dummies(df[col], prefix=col)], axis=1)
+
+    # Bin and one-hot encode continuous variables
+    for col in continuous_columns:
+        bin_labels = [f"{col}_bin_{i}" for i in range(n_bins)]
+        df[col + '_binned'] = pd.cut(df[col], bins=n_bins, labels=bin_labels)
+        encoded_df = pd.concat([encoded_df, pd.get_dummies(df[col + '_binned'])], axis=1)
+
+    # Create a dictionary with atomic numbers as keys and one-hot encoded features as values
+    element_encoding = {str(row['atomic number']): encoded_df.iloc[idx].tolist()
+                        for idx, row in df.iterrows()}
+    # Converting T/F to 1/0
+    element_encoding = {key: [int(i) for i in value] for key, value in element_encoding.items()}
+
+    # Create a dictionary for feature names
+    feature_names = {str(i): name for i, name in enumerate(encoded_df.columns)}
+
+    if filename is not None:
+        import json
+        # Save the one-hot encoding dictionary as JSON
+        with open(filename, 'w') as json_file:
+            json.dump(element_encoding, json_file)
+
+    return element_encoding, feature_names
+
+
+def get_available_features():
+    """
+    Returns a list of available features.
+    """
+    return list(pt.columns)[1:]
+
+
+if __name__ == '__main__':
+    print(get_available_features())
diff --git a/adavaria/mlband/imports.py b/adavaria/mlband/imports.py
new file mode 100644
index 00000000..c6ebfaa8
--- /dev/null
+++ b/adavaria/mlband/imports.py
@@ -0,0 +1,13 @@
+import pandas as pd
+import warnings
+import numpy as np
+from . import plot_formatter
+import importlib
+from pathlib import Path
+import random
+import sys
+import os
+import matplotlib.pyplot as plt
+import csv
+
+pd.options.plotting.backend = 'matplotlib'
diff --git a/adavaria/mlband/magpie.py b/adavaria/mlband/magpie.py
new file mode 100644
index 00000000..afcc362e
--- /dev/null
+++ b/adavaria/mlband/magpie.py
@@ -0,0 +1,62 @@
+from .imports import *
+
+
+# Data table: https://github.com/ramv2/magpie_python/tree/master/lookup-data
+
+
+def prepare_data():
+    from matminer.utils.data import MagpieData
+
+    mag = pd.DataFrame(MagpieData().all_elemental_props).reset_index().rename(columns={'index': 'symbol'})
+    mag['atomic number'] = np.arange(1, len(mag) + 1).astype(int)
+    mag.set_index('atomic number', inplace=True)
+    # mag['atomic number'] = np.arange(1, len(mag) + 1).astype(int)
+
+    # makedirs(Path('Data', 'magpie'), exist_ok=True)
+    mag.to_csv(Path(Path(__file__).parent, 'magpie_data.csv'), index=False)
+    return mag
+
+
+def get_periodic_table():
+    from pymatgen.core.periodic_table import Element
+
+    # Retrieve all elements from the periodic table provided by pymatgen
+    elements = [Element.from_Z(i) for i in range(1, 119)]
+
+    # Define rare earth elements (Sc, Y, and lanthanides), these element get 0 as their group number
+    rare_earths = [21, 39] + list(range(57, 72))  # Atomic numbers for Sc, Y, and lanthanides
+
+    # Extract the properties of each element into a DataFrame
+    df = pd.DataFrame({
+        'symbol': [e.symbol for e in elements],
+        'atomic number': [e.Z for e in elements],
+        'atomic mass': [e.atomic_mass for e in elements],
+        # 'group': [e.group for e in elements],
+        'group': [0 if e.Z in rare_earths else e.group for e in elements],
+        'period': [e.row for e in elements]  # 'row' attribute corresponds to the period
+    })
+    return df
+
+
+def get_magpie_data(fill_missing=True):
+    filename = Path(Path(__file__).parent, 'magpie_data.csv')
+    if not filename.exists():
+        df = prepare_data()
+    else:
+        df = pd.read_csv(filename)
+
+    if fill_missing:
+        # Replace NaN with 0
+        df = df.fillna(0)
+
+    pt = get_periodic_table()
+
+    # Merge the two DataFrames
+    df = pt.merge(df, on=['symbol'], how='inner')
+    return df
+
+
+if __name__ == '__main__':
+    data = prepare_data()
+    get_magpie_data()
+    pass
diff --git a/adavaria/mlband/magpie_data.csv b/adavaria/mlband/magpie_data.csv
new file mode 100644
index 00000000..46af6b17
--- /dev/null
+++ b/adavaria/mlband/magpie_data.csv
@@ -0,0 +1,119 @@
+symbol,MeltingT,MiracleRadius,CovalentRadius,FirstIonizationEnergy,GSestFCClatcnt,Electronegativity,Density,ZungerPP-r_s,DipolePolarizability,GSestBCClatcnt,LogThermalConductivity,NsValence,GSmagmom,Polarizability,NValence,IsAlkali,AtomicVolume,phi,Number,IsNonmetal,NfValence,HHIp,VdWRadius,HHIr,NpUnfilled,BulkModulus,ZungerPP-r_p,NsUnfilled,ZungerPP-r_sigma,ThermalConductivity,AtomicRadius,ShearModulus,IonizationEnergies,IsMetalloid,NfUnfilled,SecondIonizationEnergy,OxidationStates,IsDBlock,AtomicWeight,HeatVaporization,MendeleevNumber,GSvolume_pa,NUnfilled,IsMetal,ElectronAffinity,NdUnfilled,NdValence,MolarVolume,Column,ICSDVolume,IsFBlock,SpaceGroupNumber,HeatCapacityMass,AllenElectronegativity,BoilingT,GSenergy_pa,HeatCapacityMolar,n_ws^third,HeatFusion,ZungerPP-r_d,ZungerPP-r_pi,NpValence,Row,FusionEnthalpy,GSbandgap
+H,14.01,,31.0,13.598443,2.979865165,2.2,0.0899,,0.666793,2.365120549,-0.743522794,1.0,0.0,0.666793,1.0,0.0,18618.0519408073,5.2,1.0,1.0,0.0,,1.2,,0.0,,,1.0,1.25,0.1805,0.25,,13.598443,0.0,0.0,,"[-1.0, 1.0]",0.0,1.00794,0.452,92.0,6.615,1.0,0.0,72.8,0.0,0.0,11.42,1.0,32.02,0.0,194.0,14.304,2.3,20.13,-3.331290765,28.836,1.5,0.06,,,0.0,1.0,0.06,7.853
+He,,,28.0,24.587387,3.664774046,,0.1785,,0.2050522,2.908733088,-0.820161072,2.0,0.0,0.2050522,2.0,0.0,37236.0355633452,,2.0,1.0,0.0,3200.0,1.4,3900.0,0.0,,,0.0,,0.1513,,,,0.0,0.0,54.41776,[],0.0,4.002602,0.083,98.0,12.305,0.0,0.0,0.0,0.0,0.0,21.0,18.0,19.08,0.0,225.0,5.13,4.16,4.07,0.0011354,20.786,,,,,0.0,1.0,,18.098
+Li,453.69,152.0,128.0,5.391719,4.048845545,0.98,535.0,0.985,24.335,3.213570838,1.929418926,1.0,0.0,24.335,1.0,1.0,21.5440580798753,2.85,3.0,0.0,0.0,2900.0,1.82,4200.0,0.0,11.0,0.625,1.0,1.61,85.0,1.45,4.2,,0.0,0.0,75.64,[1.0],0.0,6.941,147.0,1.0,16.5933333333,1.0,1.0,59.6,0.0,0.0,13.02,1.0,21.63,0.0,229.0,3.582,0.912,1615.0,-1.86988691,24.86,0.98,3.0,,0.36,0.0,2.0,3.0,0.0
+Be,1560.0,112.0,96.0,9.3227,3.160183721,1.57,1848.0,0.64,5.6,2.508239481,2.278753601,2.0,0.0,5.6,2.0,1.0,8.09817645544979,5.05,4.0,0.0,0.0,8000.0,1.53,4000.0,0.0,130.0,0.44,0.0,1.08,190.0,1.05,132.0,,0.0,0.0,18.21114,[2.0],0.0,9.012182,297.0,67.0,7.89,0.0,1.0,0.0,0.0,0.0,4.85,2.0,8.11,0.0,194.0,1.825,1.576,2743.0,-3.755038695,16.443,1.67,7.895,,0.2,0.0,2.0,7.895,0.0
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+Fr,,,260.0,4.072741,5.370818814,0.7,,,47.85,4.262821718,,1.0,0.0,47.85,1.0,1.0,,,87.0,0.0,0.0,,3.48,,0.0,,,1.0,4.37,,,,4.072741,0.0,0.0,,[1.0],0.0,223.0,65.0,6.0,38.73125,1.0,1.0,,0.0,0.0,,1.0,79.69,0.0,,,,,-4.03934834875,,,,,,0.0,7.0,,0.0
+Ra,973.0,,221.0,5.278423,5.370818814,0.9,5000.0,,38.3,4.262821718,1.278753601,2.0,0.0,38.3,2.0,1.0,75.0581202258386,,88.0,0.0,0.0,,2.83,,0.0,,,0.0,3.53,19.0,2.15,,,0.0,0.0,10.14715,[2.0],0.0,226.0,125.0,10.0,38.73125,0.0,1.0,,0.0,0.0,41.09,2.0,68.22,0.0,229.0,,,2010.0,-4.03934834875,,,7.7,,,0.0,7.0,7.7,0.0
+Ac,1323.0,,215.0,5.17,5.625752986,1.1,10070.0,,32.1,4.465163104,1.079181246,2.0,0.0,32.1,3.0,0.0,37.4330862969509,,89.0,0.0,0.0,,,,0.0,,,0.0,3.12,12.0,1.95,,,0.0,0.0,11.75,[3.0],0.0,227.0,400.0,14.0,44.5125,9.0,1.0,,9.0,1.0,22.55,3.0,37.45,1.0,225.0,0.12,,3473.0,-4.1050022975,27.2,,12.0,,,0.0,7.0,12.0,0.0
+Th,2023.0,178.0,206.0,6.3067,5.059033597,1.3,11724.0,,32.1,4.015357627,1.73239376,2.0,0.0,32.1,4.0,0.0,32.86568321477,3.3,90.0,0.0,0.0,,,,0.0,54.0,,0.0,4.98,54.0,1.8,31.0,,0.0,0.0,11.9,[4.0],0.0,232.03806,530.0,16.0,32.37,8.0,1.0,,8.0,2.0,19.8,3.0,32.86,1.0,225.0,0.118,,5093.0,-7.41254306,27.32,1.28,13.81,,,0.0,7.0,13.81,0.0
+Pa,1845.0,165.0,200.0,5.89,4.652702018,1.5,15370.0,,25.4,3.692852039,1.672097858,2.0,0.0,25.4,5.0,0.0,24.9611606283359,,91.0,0.0,2.0,,,,0.0,,,0.0,4.86,47.0,1.8,,5.89,0.0,12.0,,"[4.0, 5.0]",0.0,231.03586,470.0,18.0,25.18,21.0,1.0,,9.0,1.0,15.18,3.0,24.94,1.0,139.0,,,4273.0,-9.49741247,,,12.34,,,0.0,7.0,12.34,0.0
+U,1408.0,158.0,196.0,6.1941,4.310663995,1.38,19050.0,,24.9,3.42137628,1.431363764,2.0,0.0,24.9,6.0,0.0,20.7488474020455,3.9,92.0,0.0,3.0,,1.86,,0.0,100.0,,0.0,4.72,27.0,1.75,111.0,,0.0,11.0,10.6,"[3.0, 4.0, 5.0, 6.0]",0.0,238.02891,420.0,20.0,20.025,20.0,1.0,,9.0,1.0,12.49,3.0,21.18,1.0,63.0,0.116,,4200.0,-11.292339955,27.665,1.51,9.14,,,0.0,7.0,9.14,0.0
+Np,917.0,175.0,190.0,6.2657,4.194834921,1.36,20450.0,,24.8,3.329442683,0.77815125,2.0,0.0,24.8,7.0,0.0,19.2448390132676,,93.0,0.0,4.0,,,,0.0,,,0.0,4.83,6.0,1.75,,6.2657,0.0,10.0,,"[3.0, 4.0, 5.0, 6.0]",0.0,237.0,335.0,22.0,18.45375,19.0,1.0,,9.0,1.0,11.59,3.0,19.22,1.0,62.0,,,4273.0,-12.9403143562,,,3.2,,,0.0,7.0,3.2,0.0
+Pu,913.0,175.0,187.0,6.026,4.166321749,1.28,19816.0,,24.5,3.306811764,0.77815125,2.0,0.3180036375,24.5,8.0,0.0,20.4471640587929,3.8,94.0,0.0,6.0,,,,0.0,,,0.0,4.91,6.0,1.75,43.0,,0.0,8.0,11.2,"[3.0, 4.0, 5.0, 6.0]",0.0,244.0,325.0,24.0,18.08,8.0,1.0,,0.0,0.0,12.29,3.0,20.04,1.0,11.0,,,3503.0,-14.326634305,,1.44,2.824,,,0.0,7.0,2.824,0.0
+Am,1449.0,,180.0,5.9738,4.166321749,1.3,13670.0,,23.3,3.306811764,1.0,2.0,0.3180036375,23.3,9.0,0.0,29.5186850847065,,95.0,0.0,7.0,,,,0.0,,,0.0,4.89,10.0,1.75,,5.9738,0.0,7.0,,"[3.0, 4.0, 5.0, 6.0]",0.0,243.0,,26.0,18.08,7.0,1.0,,0.0,0.0,17.63,3.0,29.27,1.0,194.0,,,2284.0,-14.326634305,,,14.39,,,0.0,7.0,14.39,0.0
+Cm,1618.0,,169.0,5.9914,4.166321749,1.3,13510.0,,23.0,3.306811764,0.944482672,2.0,0.3180036375,23.0,10.0,0.0,30.3599360941782,,96.0,0.0,7.0,,,,0.0,,,0.0,,8.8,,,5.9914,0.0,7.0,,[3.0],0.0,247.0,,28.0,18.08,16.0,1.0,,9.0,1.0,18.05,3.0,21.04,1.0,194.0,,,3383.0,-14.326634305,,,14.64,,,0.0,7.0,14.64,0.0
+Bk,1323.0,,,6.1979,4.166321749,1.3,14780.0,,22.7,3.306811764,1.0,2.0,0.3180036375,22.7,11.0,0.0,27.751200042784,,97.0,0.0,9.0,,,,0.0,,,0.0,,10.0,,,6.1979,0.0,5.0,,"[3.0, 4.0]",0.0,247.0,,30.0,18.08,5.0,1.0,,0.0,0.0,16.84,3.0,31.2,1.0,194.0,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+Cf,1173.0,,,6.2817,4.166321749,1.3,15100.0,,20.5,3.306811764,,2.0,0.3180036375,20.5,12.0,0.0,27.6029833216396,,98.0,0.0,10.0,,,,0.0,,,0.0,,,,,,0.0,4.0,11.8,[3.0],0.0,251.0,,32.0,18.08,4.0,1.0,,0.0,0.0,16.5,3.0,27.31,1.0,194.0,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+Es,1133.0,,,6.42,4.166321749,1.3,,,19.7,3.306811764,,2.0,0.3180036375,19.7,13.0,0.0,,,99.0,0.0,11.0,,,,0.0,,,0.0,,,,,,0.0,3.0,12.0,[3.0],0.0,252.0,,34.0,18.08,3.0,1.0,,0.0,0.0,28.52,3.0,27.31,1.0,,,,,-14.326634305,,,9.41,,,0.0,7.0,9.41,0.0
+Fm,1800.0,,,6.5,4.166321749,1.3,,,23.8,3.306811764,,2.0,0.3180036375,23.8,14.0,0.0,,,100.0,0.0,12.0,,,,0.0,,,0.0,,,,,6.5,0.0,2.0,,[3.0],0.0,257.0,,36.0,18.08,2.0,1.0,,0.0,0.0,,3.0,27.31,1.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+Md,1100.0,,,6.58,4.166321749,1.3,,,18.2,3.306811764,,2.0,0.3180036375,18.2,15.0,0.0,,,101.0,0.0,13.0,,,,0.0,,,0.0,,,,,6.58,0.0,1.0,,[3.0],0.0,258.0,,38.0,18.08,1.0,1.0,,0.0,0.0,,3.0,27.31,1.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+No,1100.0,,,6.65,4.166321749,1.3,,,16.4,3.306811764,,2.0,0.3180036375,16.4,16.0,0.0,,,102.0,0.0,14.0,,,,0.0,,,0.0,,,,,6.65,0.0,0.0,,[3.0],0.0,259.0,,40.0,18.08,0.0,1.0,,0.0,0.0,,3.0,27.31,1.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+Lr,1900.0,,,4.9,4.166321749,,,,,3.306811764,,2.0,0.3180036375,,17.0,0.0,,,103.0,0.0,14.0,,,,5.0,,,0.0,,,,,4.9,0.0,0.0,,[3.0],1.0,262.0,,42.0,18.08,5.0,1.0,,0.0,0.0,,3.0,27.31,1.0,,,,,-14.326634305,,,,,,1.0,7.0,,0.0
+Rf,,,,6.0,4.166321749,,,,,3.306811764,,2.0,0.3180036375,,18.0,0.0,,,104.0,0.0,14.0,,,,0.0,,,0.0,,,,,6.0,0.0,0.0,,[4.0],1.0,265.0,,,18.08,8.0,1.0,,8.0,2.0,,4.0,27.31,0.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+Db,,,,,4.166321749,,,,,3.306811764,,2.0,0.3180036375,,19.0,0.0,,,105.0,0.0,14.0,,,,0.0,,,0.0,,,,,,0.0,0.0,,[5.0],1.0,268.0,,,18.08,7.0,1.0,,7.0,3.0,,5.0,27.31,0.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+Sg,,,,,4.166321749,,,,,3.306811764,,2.0,0.3180036375,,20.0,0.0,,,106.0,0.0,14.0,,,,0.0,,,0.0,,,,,,0.0,0.0,,[6.0],1.0,271.0,,,18.08,6.0,1.0,,6.0,4.0,,6.0,27.31,0.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+Bh,,,,,4.166321749,,,,,3.306811764,,2.0,0.3180036375,,21.0,0.0,,,107.0,0.0,14.0,,,,0.0,,,0.0,,,,,,0.0,0.0,,"[4.0, 6.0, 7.0]",1.0,272.0,,,18.08,5.0,1.0,,5.0,5.0,,7.0,27.31,0.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
+Hs,,,,,4.166321749,,,,,3.306811764,,2.0,0.3180036375,,22.0,0.0,,,108.0,0.0,14.0,,,,0.0,,,0.0,,,,,,0.0,0.0,,"[3.0, 4.0]",1.0,270.0,,,18.08,4.0,1.0,,4.0,6.0,,8.0,27.31,0.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
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+Ds,,,,,4.166321749,,,,,3.306811764,,1.0,0.3180036375,,24.0,0.0,,,110.0,0.0,14.0,,,,0.0,,,1.0,,,,,,0.0,0.0,,"[2.0, 4.0]",1.0,281.0,,,18.08,2.0,1.0,,1.0,9.0,,10.0,27.31,0.0,,,,,-14.326634305,,,,,,0.0,7.0,,0.0
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+Nh,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Fl,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Mc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Lv,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Ts,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Og,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/adavaria/mlband/main.py b/adavaria/mlband/main.py
new file mode 100644
index 00000000..27250089
--- /dev/null
+++ b/adavaria/mlband/main.py
@@ -0,0 +1,37 @@
+from mlband.imports import *
+import mlband.data
+import mlband.config
+import mlband.model
+import mlband.features
+
+
+def main(num_chunks=1):
+    data_set_path = 'data/mp-test-2/'
+    results_dir = 'results/test-2/'
+
+    print('Preparing the config...', flush=True)
+    config = mlband.config.Config(
+        data_path=data_set_path, 
+        workers=4, 
+        results_dir=results_dir,
+        original_features=True,
+        )
+
+    print('Downloading the data from MP...' ,flush=True)
+    df, data = mlband.data.get_list_of_materials(num_chunks=1)
+
+    print('Creating the dataset...', flush=True)
+    mlband.data.create_dataset(df=df, path=data_set_path)
+
+    print('Creating the physical features...', flush=True)
+    mlband.features.one_hot_encode_elements(config)
+    
+    print('Creating and training the model...', flush=True)
+    mlband.model.train(config)
+
+    print('Evaluating the training set...', flush=True)
+    mlband.model.evaluate_model(config)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/adavaria/mlband/model.py b/adavaria/mlband/model.py
new file mode 100644
index 00000000..fd254bbd
--- /dev/null
+++ b/adavaria/mlband/model.py
@@ -0,0 +1,271 @@
+from .imports import *
+import subprocess
+import mlband.data
+import torch
+import torch.nn as nn
+from . import utility
+
+
+# cgcnn_path = Path('./cgcnn/').absolute().__str__()
+# if cgcnn_path not in sys.path:
+#     sys.path.append(cgcnn_path)
+
+
+def train_by_original_code(config, ignore_warnings=True):
+    """
+    Training the model. Usage example:
+    config = Config(epochs=50, learning_rate=0.005, batch_size=128)
+    train(config)
+
+    When using this function, cgccn must be cloned in the parent directory of mlband.
+    git clone https://github.com/txie-93/cgcnn.git
+    """
+    print('Training the model...')
+    results_dir = config.results_dir
+    if not os.path.exists(results_dir):
+        os.makedirs(results_dir)
+
+    # Add the cgcnn path to the system path
+    cgcnn_path = Path('./cgcnn/').absolute().__str__()
+    if cgcnn_path not in sys.path:
+        sys.path.append(cgcnn_path)
+
+    # ignore warnings
+    if ignore_warnings:
+        os.environ['PYTHONWARNINGS'] = "ignore"
+
+    # Define the command and arguments
+    # script_path = Path('./cgcnn/main.py').absolute().__str__()
+    # script_path based on the relative path of this file
+    script_path = Path(__file__).parent / 'cgcnn' / 'main.py'
+    args = config.generate_args()
+    command = ['python', script_path] + args
+
+    # Start the process
+    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, cwd=results_dir)
+
+    # Continuously showing the output
+    while True:
+        output = process.stdout.readline()
+        if not output and process.poll() is not None:
+            break
+        if output:
+            print(output.strip())
+
+    # Close the stdout and waiting for the process to terminate
+    process.stdout.close()
+    return_code = process.wait()  # Capture the return code of the process
+
+    # Check if the subprocess was successful
+    if return_code != 0:
+        raise Exception(f"Subprocess failed with return code {return_code}")
+
+    print('Training and test evaluation completed!')
+
+
+def train(config, ignore_warnings=True):
+    """
+    Training the model. Usage example:
+    config = Config(epochs=50, learning_rate=0.005, batch_size=128)
+    train(config)
+    """
+    print('Training the model...')
+    args = config
+    results_dir = config.results_dir
+    Path(results_dir).mkdir(parents=True, exist_ok=True)
+
+    if args.task == 'regression':
+        best_mae_error = 1e10
+    else:
+        best_mae_error = 0.
+
+    train_loader, val_loader, test_loader = mlband.data.get_data_loaders(config)
+    normalizer = get_normalizer(config)
+    model = get_cgcnn_model(config)
+    criterion = nn.MSELoss()
+    optimizer = get_optimizer(config, model)
+    from torch.optim.lr_scheduler import MultiStepLR
+    scheduler = MultiStepLR(optimizer, milestones=args.lr_milestones,
+                            gamma=0.1)
+    # Train the model
+    for epoch in range(args.start_epoch, args.epochs):
+        from .cgcnn.train import train_epoch, validate, save_checkpoint
+        # train for one epoch
+        train_epoch(config, train_loader, model, criterion, optimizer, epoch, normalizer)
+
+        # evaluate on validation set
+        mae_error = validate(config, val_loader, model, criterion, normalizer)
+
+        if mae_error != mae_error:
+            print('Exit due to NaN')
+            sys.exit(1)
+
+        scheduler.step()
+
+        # remember the best mae_eror and save checkpoint
+        if args.task == 'regression':
+            is_best = mae_error < best_mae_error
+            best_mae_error = min(mae_error, best_mae_error)
+        else:
+            is_best = mae_error > best_mae_error
+            best_mae_error = max(mae_error, best_mae_error)
+        save_checkpoint({
+            'epoch': epoch + 1,
+            'state_dict': model.state_dict(),
+            'best_mae_error': best_mae_error,
+            'optimizer': optimizer.state_dict(),
+            'normalizer': normalizer.state_dict(),
+            'args': vars(args)
+        }, is_best, cd=results_dir)
+
+    print('Training and test evaluation completed!')
+
+
+def get_cgcnn_model(args,
+                    orig_atom_fea_len=None,
+                    nbr_fea_len=None,
+                    atom_fea_len=None,
+                    n_conv=None,
+                    h_fea_len=None,
+                    n_h=None,
+                    ):
+    from .cgcnn.data import CIFData
+    from .cgcnn.model import CrystalGraphConvNet
+
+    if orig_atom_fea_len is None or nbr_fea_len is None:
+        dataset = CIFData(args.data_path)
+        with utility.mute_warnings():
+            structures, _, _ = dataset[0]
+        orig_atom_fea_len = structures[0].shape[-1]
+        nbr_fea_len = structures[1].shape[-1]
+
+    model = CrystalGraphConvNet(orig_atom_fea_len, nbr_fea_len,
+                                atom_fea_len=atom_fea_len or args.atom_fea_len,
+                                n_conv=n_conv or args.n_conv,
+                                h_fea_len=h_fea_len or args.h_fea_len,
+                                n_h=n_h or args.n_h,
+                                classification=False)
+    if args.cuda:
+        model.cuda()
+    return model
+
+
+def get_normalizer(config, ignore_warnings=True):
+    from .cgcnn.data import CIFData, collate_pool
+    from .cgcnn.utility import Normalizer
+    from random import sample
+
+    dataset = CIFData(config.data_path)
+
+    with utility.mute_warnings(enabled=ignore_warnings):
+        if len(dataset) < 500:
+            warnings.warn('Dataset has less than 500 data points. '
+                        'Lower accuracy is expected. ')
+            sample_data_list = [dataset[i] for i in range(len(dataset))]
+        else:
+            sample_data_list = [dataset[i] for i in
+                                sample(range(len(dataset)), 500)]
+        _, sample_target, _ = collate_pool(sample_data_list)
+        normalizer = Normalizer(sample_target)
+    return normalizer
+
+
+def load_model(config, orig_atom_fea_len=None, nbr_fea_len=None,):
+    from .cgcnn.utility import Normalizer
+    # Load the model
+    best_checkpoint = torch.load(config.results_dir / 'model_best.pth.tar')
+    model = get_cgcnn_model(config, orig_atom_fea_len=orig_atom_fea_len, nbr_fea_len=nbr_fea_len)
+    model.load_state_dict(best_checkpoint['state_dict'])
+    normalizer = Normalizer(torch.zeros(3))
+    normalizer.load_state_dict(best_checkpoint['normalizer'])
+    return model, normalizer
+
+
+def evaluate_model(config):
+    # Gettting the data loaders
+    train_loader, val_loader, test_loader = mlband.data.get_data_loaders(config)
+
+    # # Load the model
+    # best_checkpoint = torch.load(config.results_dir/'model_best.pth.tar')
+    # model = get_cgcnn_model(config)
+    # model.load_state_dict(best_checkpoint['state_dict'])
+    # normalizer = utility.Normalizer(torch.zeros(3))
+    # normalizer.load_state_dict(best_checkpoint['normalizer'])
+    # # criterion = nn.MSELoss()
+    model, normalizer = load_model(config)
+
+    # Evaluate the model
+    for loader, name in zip([train_loader, val_loader, test_loader], ['train', 'val', 'test']):
+        print('Evaluating the {} set...'.format(name))
+        output_filename = os.path.join(config.results_dir, 'predictions_{}.csv'.format(name))
+        df = predict_model(loader, model, normalizer, output_filename=output_filename)
+        # print mse
+        import sklearn.metrics
+        mse = sklearn.metrics.mean_squared_error(df['True_Label'], df['Prediction'])
+        mae = sklearn.metrics.mean_absolute_error(df['True_Label'], df['Prediction'])
+        print(f'MSE for {name} set: {mse:.4f}')
+        print(f'MAE for {name} set: {mae:.4f}')
+
+
+def predict_model(loader, model, normalizer, output_filename=None, mute_warnings=True):
+    model.eval()  # Set the model to evaluation mode
+    predictions = []
+    true_labels = []
+    ids = []
+
+    with torch.no_grad(), utility.mute_warnings(enabled=mute_warnings):
+        for i, (inp, labels, batch_ids) in enumerate(loader):
+            # Assuming your model and data are on the same device (CPU or CUDA)
+            input_var = (inp[0], inp[1], inp[2], inp[3])
+
+            # Compute output
+            output = model(*input_var)
+            denormed_output = normalizer.denorm(output.cpu())
+
+            predictions.extend(denormed_output.view(-1).tolist())
+            # Convert labels to scalar values
+            if labels.dim() > 0:
+                true_labels.extend(labels.squeeze().tolist())
+            else:
+                true_labels.append(labels.item())
+            ids.extend(batch_ids)  # Assuming batch_ids is a list or similar
+    
+    df = pd.DataFrame({
+        'ID': ids,
+        'True_Label': true_labels,
+        'Prediction': predictions
+    })
+    if output_filename is not None:
+        # Save the predictions, true labels, and IDs to a CSV file
+        print('Saving the predictions to {}'.format(output_filename))
+        df.to_csv(output_filename, index=False)
+    return df
+
+
+def get_optimizer(config, model):
+    import torch.optim as optim
+    args = config
+    if args.optim == 'SGD':
+        optimizer = optim.SGD(model.parameters(), args.learning_rate,
+                              momentum=args.momentum,
+                              weight_decay=args.weight_decay)
+    elif args.optim == 'Adam':
+        optimizer = optim.Adam(model.parameters(), args.learning_rate,
+                               weight_decay=args.weight_decay)
+    else:
+        raise NotImplementedError
+
+    return optimizer
+
+
+def predict(data_path, config=None, model=None, normalizer=None):
+    if config is None:
+        from .config import Config
+        model_dir = Path(__file__).parent / 'pretrained_model'
+        config = Config(data_path=data_path, results_dir=model_dir)
+
+    data_loader = mlband.data.get_data_loader(config)
+    if model is None or normalizer is None:
+        model, normalizer = load_model(config)
+    df = predict_model(data_loader, model, normalizer)
+    return df
diff --git a/adavaria/mlband/mp.py b/adavaria/mlband/mp.py
new file mode 100644
index 00000000..f5e1e3fb
--- /dev/null
+++ b/adavaria/mlband/mp.py
@@ -0,0 +1,24 @@
+import pandas as pd
+import warnings
+from pymatgen.io.cif import CifWriter
+
+
+def get_mp_api_connector():
+    from mp_api.client import MPRester
+    api_key = 'pJmFwIk6qWCnMwEuGvzwLBT1qfdPpElH'
+    return MPRester(api_key)
+
+
+mpr = None
+try:
+    mpr = get_mp_api_connector()
+except:
+    warnings.warn('Could not connect to MP API.')
+    pass
+
+
+def mp_summary_to_df(data, drop_fields_not_requested=True):
+    df = pd.DataFrame([doc.dict() for doc in data])
+    if drop_fields_not_requested:
+        df.drop(columns=['fields_not_requested'], inplace=True)
+    return df
diff --git a/adavaria/mlband/plot_formatter.py b/adavaria/mlband/plot_formatter.py
new file mode 100644
index 00000000..a94e4d44
--- /dev/null
+++ b/adavaria/mlband/plot_formatter.py
@@ -0,0 +1,61 @@
+# plot_formatting.py
+import matplotlib.pyplot as plt
+import matplotlib
+# import seaborn as sns
+import pandas as pd
+
+# Defining all the parameters for the plots
+font_size = 9
+golden_ratio = (5 ** .5 - 1) / 2
+mm_to_inch = 0.0393701
+one_column = 90 * mm_to_inch
+two_column = 190 * mm_to_inch
+
+figsize = {
+    'one_column': (one_column, one_column * golden_ratio),
+    'two_column': (two_column, two_column * golden_ratio),
+    'one_column_square': (one_column, one_column),
+    'two_column_square': (two_column, two_column),
+    'one_column_wide': (one_column, one_column * golden_ratio * 2),
+    'two_column_wide': (two_column, two_column * golden_ratio * 2),
+}
+figsize['default'] = figsize['one_column']
+
+# Assigning the parameters to the matplotlib and seaborn
+params = {
+    "legend.fontsize": font_size,
+    "xtick.labelsize": font_size,
+    "ytick.labelsize": font_size,
+    'font.size': font_size,
+    'axes.labelsize': font_size,
+    'axes.titlesize': font_size + 1,
+    'grid.color': 'silver',
+    'grid.linestyle': '--',
+    'grid.linewidth': 1,
+    'grid.alpha': 0.45,
+    'axes.grid': False,
+    'xtick.bottom': True,
+    'ytick.left': True,
+}
+matplotlib.rcParams.update(params)
+
+plt.rcParams['xtick.bottom'] = True  # Show x-axis ticks at the bottom
+plt.rcParams['ytick.left'] = True  # Show y-axis ticks on the left
+
+matplotlib.rc('font', family='sans-serif')
+matplotlib.rc('font', serif='Helvetica')
+matplotlib.rc('text', usetex='false')
+matplotlib.rcParams['svg.fonttype'] = 'none'
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['lines.linewidth'] = 2
+matplotlib.rcParams['axes.titlesize'] = font_size + 1
+
+# # Update the parameters for tick labels format to be comma separated
+# plt.rcParams['axes.formatter.use_locale'] = True
+# plt.rcParams['axes.formatter.use_mathtext'] = True
+
+plt.rcParams['figure.figsize'] = figsize['default']
+plt.rcParams['figure.dpi'] = 100
+plt.rcParams['savefig.dpi'] = 600
+plt.rcParams['savefig.bbox'] = 'tight'
diff --git a/adavaria/mlband/pretrained_model/checkpoint.pth.tar b/adavaria/mlband/pretrained_model/checkpoint.pth.tar
new file mode 100644
index 00000000..f802c0b0
Binary files /dev/null and b/adavaria/mlband/pretrained_model/checkpoint.pth.tar differ
diff --git a/adavaria/mlband/pretrained_model/model_best.pth.tar b/adavaria/mlband/pretrained_model/model_best.pth.tar
new file mode 100644
index 00000000..efb5d6c6
Binary files /dev/null and b/adavaria/mlband/pretrained_model/model_best.pth.tar differ
diff --git a/adavaria/mlband/pretrained_model/predictions_test.csv b/adavaria/mlband/pretrained_model/predictions_test.csv
new file mode 100644
index 00000000..e7757aef
--- /dev/null
+++ b/adavaria/mlband/pretrained_model/predictions_test.csv
@@ -0,0 +1,4000 @@
+ID,True_Label,Prediction
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diff --git a/adavaria/mlband/pretrained_model/predictions_train.csv b/adavaria/mlband/pretrained_model/predictions_train.csv
new file mode 100644
index 00000000..98ff7e74
--- /dev/null
+++ b/adavaria/mlband/pretrained_model/predictions_train.csv
@@ -0,0 +1,31999 @@
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diff --git a/adavaria/mlband/pretrained_model/predictions_val.csv b/adavaria/mlband/pretrained_model/predictions_val.csv
new file mode 100644
index 00000000..6eee3751
--- /dev/null
+++ b/adavaria/mlband/pretrained_model/predictions_val.csv
@@ -0,0 +1,4000 @@
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diff --git a/adavaria/mlband/utility.py b/adavaria/mlband/utility.py
new file mode 100644
index 00000000..519eb989
--- /dev/null
+++ b/adavaria/mlband/utility.py
@@ -0,0 +1,102 @@
+import torch
+import contextlib
+
+
+# class Normalizer(object):
+#     """
+#     Normalize a Tensor and restore it later. 
+#     Taken from: https://github.com/txie-93/cgcnn
+#     """
+
+#     def __init__(self, tensor=torch.zeros(3)):
+#         """tensor is taken as a sample to calculate the mean and std"""
+#         self.mean = torch.mean(tensor)
+#         self.std = torch.std(tensor)
+
+#     def norm(self, tensor):
+#         """Normalize a tensor"""
+#         return (tensor - self.mean) / self.std
+
+#     def denorm(self, normed_tensor):
+#         """Denormalize a normalized tensor"""
+#         return normed_tensor * self.std + self.mean
+
+#     def state_dict(self):
+#         """Returns a dictionary containing a whole state of the module."""
+#         return {'mean': self.mean,
+#                 'std': self.std}
+
+#     def load_state_dict(self, state_dict):
+#         """Loads a state dict."""
+#         self.mean = state_dict['mean']
+#         self.std = state_dict['std']
+#         return self
+
+
+# class AverageMeter(object):
+#     """
+#     Computes and stores the average and current value.
+#     Taken from: https://github.com/txie-93/cgcnn
+#     """
+
+#     def __init__(self):
+#         self.reset()
+
+#     def reset(self):
+#         self.val = 0
+#         self.avg = 0
+#         self.sum = 0
+#         self.count = 0
+
+#     def update(self, val, n=1):
+#         self.val = val
+#         self.sum += val * n
+#         self.count += n
+#         self.avg = self.sum / self.count
+
+
+@contextlib.contextmanager
+def mute_warnings(enabled=True):
+    """
+    A context manager that temporarily suppresses warnings.
+    Usage:
+        with mute_warnings():
+            warnings.warn('This will not be printed')
+    """
+    import warnings
+    if enabled:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+
+            # Add this block to configure logging for the pint library
+            import logging
+            pint_logger = logging.getLogger('pint')
+            previous_level = pint_logger.getEffectiveLevel()
+            pint_logger.setLevel(logging.ERROR)
+
+            yield
+
+            pint_logger.setLevel(previous_level)  # Reset the logging level
+    else:
+        yield
+
+def mae(prediction, target):
+    return torch.mean(torch.abs(target - prediction))
+
+def mse(prediction, target):
+    return torch.mean((target - prediction)**2)
+
+# def adjust_learning_rate(optimizer, epoch, k, lr):
+#     """Sets the learning rate to the initial LR decayed by 10 every k epochs"""
+#     assert type(k) is int
+#     lr = lr * (0.1 ** (epoch // k))
+#     for param_group in optimizer.param_groups:
+#         param_group['lr'] = lr
+
+# def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
+#     """Saves checkpoint to disk"""
+#     torch.save(state, filename)
+#     if is_best:
+#         import shutil
+#         shutil.copyfile(filename, 'model_best.pth.tar')
+
diff --git a/adavaria/setup.py b/adavaria/setup.py
new file mode 100644
index 00000000..a607b66c
--- /dev/null
+++ b/adavaria/setup.py
@@ -0,0 +1,25 @@
+from setuptools import setup, find_packages
+import os
+from pathlib import Path
+
+
+def package_files(directory, pattern='**/*'):
+    paths = []
+    for filepath in Path(directory).glob(pattern):
+        if filepath.is_file():
+            paths.append(str(filepath.relative_to(directory)))
+    return paths
+
+setup(
+    name='mlband',
+    version=0.01,
+    packages=find_packages(),
+    package_data={
+        # 'mlband': ['atom_init.json', 'magpie_data.csv'],
+        'mlband': ['pretrained_model/*', 'atom_init.json', 'magpie_data.csv'],
+    },
+    author='Ali Davariashtiyani',
+    author_email='a.davari871@gmail.com',
+    description='CGCNN for band gap prediction',
+    # python_requires='>=3.',
+)
\ No newline at end of file
diff --git a/adavaria/train_cgcnn.ipynb b/adavaria/train_cgcnn.ipynb
new file mode 100644
index 00000000..e2dd55f0
--- /dev/null
+++ b/adavaria/train_cgcnn.ipynb
@@ -0,0 +1,476 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlband.imports import *\n",
+    "import mlband.data\n",
+    "import mlband.config\n",
+    "import mlband.model\n",
+    "import mlband.features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preparing the config...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_set_path = 'data/mp-test-2/'\n",
+    "results_dir = 'results/test-2/'\n",
+    "\n",
+    "print('Preparing the config...', flush=True)\n",
+    "config = mlband.config.Config(\n",
+    "    data_path=data_set_path, \n",
+    "    workers=4, \n",
+    "    results_dir=results_dir,\n",
+    "    original_features=True,\n",
+    "    print_freq=100,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading the data from MP...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f72a750e356046d5889a6a20c2e52239",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Retrieving SummaryDoc documents:   0%|          | 0/40000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of missing values:  2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Downloading the data from MP...' ,flush=True)\n",
+    "df, data = mlband.data.get_list_of_materials(num_chunks=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating the dataset...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ali/mambaforge/envs/cgcnn/lib/python3.11/site-packages/pymatgen/core/periodic_table.py:120: UserWarning: No Pauling electronegativity for Ar. Setting to NaN. This has no physical meaning, and is mainly done to avoid errors caused by the code expecting a float.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Creating the dataset...', flush=True)\n",
+    "mlband.data.create_dataset(df=df, path=data_set_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating the physical features...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Creating the physical features...', flush=True)\n",
+    "mlband.features.one_hot_encode_elements(config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating and training the model...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training the model...\n",
+      "Epoch: [0][0/500]\tTime 21.107 (21.107)\tData 17.464 (17.464)\tLoss 1.0621 (1.0621)\tMAE 1.037 (1.037)\n",
+      "Test: [0/63]\tTime 8.918 (8.918)\tLoss 0.4582 (0.4582)\tMAE 0.736 (0.736)\n",
+      "* MAE 0.758\n",
+      "Epoch: [1][0/500]\tTime 17.260 (17.260)\tData 14.009 (14.009)\tLoss 0.5957 (0.5957)\tMAE 0.835 (0.835)\n",
+      "Test: [0/63]\tTime 11.642 (11.642)\tLoss 0.4780 (0.4780)\tMAE 0.625 (0.625)\n",
+      "* MAE 0.693\n",
+      "Epoch: [2][0/500]\tTime 16.469 (16.469)\tData 13.378 (13.378)\tLoss 0.4486 (0.4486)\tMAE 0.663 (0.663)\n",
+      "Test: [0/63]\tTime 13.246 (13.246)\tLoss 0.4347 (0.4347)\tMAE 0.625 (0.625)\n",
+      "* MAE 0.634\n",
+      "Epoch: [3][0/500]\tTime 16.687 (16.687)\tData 13.987 (13.987)\tLoss 0.4238 (0.4238)\tMAE 0.578 (0.578)\n",
+      "Test: [0/63]\tTime 12.243 (12.243)\tLoss 0.4460 (0.4460)\tMAE 0.589 (0.589)\n",
+      "* MAE 0.692\n",
+      "Epoch: [4][0/500]\tTime 15.792 (15.792)\tData 13.262 (13.262)\tLoss 0.2359 (0.2359)\tMAE 0.473 (0.473)\n",
+      "Test: [0/63]\tTime 11.502 (11.502)\tLoss 0.4708 (0.4708)\tMAE 0.691 (0.691)\n",
+      "* MAE 0.596\n",
+      "Epoch: [5][0/500]\tTime 16.802 (16.802)\tData 13.959 (13.959)\tLoss 0.3425 (0.3425)\tMAE 0.597 (0.597)\n",
+      "Test: [0/63]\tTime 12.731 (12.731)\tLoss 0.3664 (0.3664)\tMAE 0.595 (0.595)\n",
+      "* MAE 0.570\n",
+      "Epoch: [6][0/500]\tTime 16.460 (16.460)\tData 13.763 (13.763)\tLoss 0.2584 (0.2584)\tMAE 0.506 (0.506)\n",
+      "Test: [0/63]\tTime 13.052 (13.052)\tLoss 0.3243 (0.3243)\tMAE 0.583 (0.583)\n",
+      "* MAE 0.571\n",
+      "Epoch: [7][0/500]\tTime 16.119 (16.119)\tData 13.650 (13.650)\tLoss 0.2520 (0.2520)\tMAE 0.509 (0.509)\n",
+      "Test: [0/63]\tTime 12.259 (12.259)\tLoss 0.3185 (0.3185)\tMAE 0.625 (0.625)\n",
+      "* MAE 0.557\n",
+      "Epoch: [8][0/500]\tTime 15.440 (15.440)\tData 13.341 (13.341)\tLoss 0.4828 (0.4828)\tMAE 0.714 (0.714)\n",
+      "Test: [0/63]\tTime 13.234 (13.234)\tLoss 0.5613 (0.5613)\tMAE 0.753 (0.753)\n",
+      "* MAE 0.532\n",
+      "Epoch: [9][0/500]\tTime 17.608 (17.608)\tData 14.073 (14.073)\tLoss 0.3108 (0.3108)\tMAE 0.607 (0.607)\n",
+      "Test: [0/63]\tTime 12.735 (12.735)\tLoss 0.4146 (0.4146)\tMAE 0.639 (0.639)\n",
+      "* MAE 0.517\n",
+      "Epoch: [10][0/500]\tTime 17.894 (17.894)\tData 14.771 (14.771)\tLoss 0.3295 (0.3295)\tMAE 0.536 (0.536)\n",
+      "Test: [0/63]\tTime 12.174 (12.174)\tLoss 0.1863 (0.1863)\tMAE 0.436 (0.436)\n",
+      "* MAE 0.529\n",
+      "Epoch: [11][0/500]\tTime 16.214 (16.214)\tData 13.744 (13.744)\tLoss 0.2154 (0.2154)\tMAE 0.486 (0.486)\n",
+      "Test: [0/63]\tTime 12.534 (12.534)\tLoss 0.4862 (0.4862)\tMAE 0.582 (0.582)\n",
+      "* MAE 0.495\n",
+      "Epoch: [12][0/500]\tTime 16.875 (16.875)\tData 13.855 (13.855)\tLoss 0.1784 (0.1784)\tMAE 0.391 (0.391)\n",
+      "Test: [0/63]\tTime 11.520 (11.520)\tLoss 0.1782 (0.1782)\tMAE 0.484 (0.484)\n",
+      "* MAE 0.568\n",
+      "Epoch: [13][0/500]\tTime 17.819 (17.819)\tData 14.870 (14.870)\tLoss 0.2968 (0.2968)\tMAE 0.589 (0.589)\n",
+      "Test: [0/63]\tTime 12.410 (12.410)\tLoss 0.2117 (0.2117)\tMAE 0.402 (0.402)\n",
+      "* MAE 0.498\n",
+      "Epoch: [14][0/500]\tTime 17.500 (17.500)\tData 13.961 (13.961)\tLoss 0.3027 (0.3027)\tMAE 0.469 (0.469)\n",
+      "Test: [0/63]\tTime 13.770 (13.770)\tLoss 0.2684 (0.2684)\tMAE 0.476 (0.476)\n",
+      "* MAE 0.510\n",
+      "Epoch: [15][0/500]\tTime 15.880 (15.880)\tData 13.407 (13.407)\tLoss 0.2566 (0.2566)\tMAE 0.568 (0.568)\n",
+      "Test: [0/63]\tTime 11.490 (11.490)\tLoss 0.4080 (0.4080)\tMAE 0.468 (0.468)\n",
+      "* MAE 0.497\n",
+      "Epoch: [16][0/500]\tTime 17.515 (17.515)\tData 14.617 (14.617)\tLoss 0.1734 (0.1734)\tMAE 0.420 (0.420)\n",
+      "Test: [0/63]\tTime 11.858 (11.858)\tLoss 0.2879 (0.2879)\tMAE 0.507 (0.507)\n",
+      "* MAE 0.472\n",
+      "Epoch: [17][0/500]\tTime 17.691 (17.691)\tData 14.675 (14.675)\tLoss 0.2464 (0.2464)\tMAE 0.501 (0.501)\n",
+      "Test: [0/63]\tTime 13.971 (13.971)\tLoss 0.2274 (0.2274)\tMAE 0.462 (0.462)\n",
+      "* MAE 0.460\n",
+      "Epoch: [18][0/500]\tTime 16.739 (16.739)\tData 14.427 (14.427)\tLoss 0.2131 (0.2131)\tMAE 0.471 (0.471)\n",
+      "Test: [0/63]\tTime 12.042 (12.042)\tLoss 0.3554 (0.3554)\tMAE 0.576 (0.576)\n",
+      "* MAE 0.501\n",
+      "Epoch: [19][0/500]\tTime 17.114 (17.114)\tData 14.140 (14.140)\tLoss 0.1652 (0.1652)\tMAE 0.454 (0.454)\n",
+      "Test: [0/63]\tTime 12.444 (12.444)\tLoss 0.3312 (0.3312)\tMAE 0.548 (0.548)\n",
+      "* MAE 0.452\n",
+      "Epoch: [20][0/500]\tTime 16.996 (16.996)\tData 14.147 (14.147)\tLoss 0.1962 (0.1962)\tMAE 0.432 (0.432)\n",
+      "Test: [0/63]\tTime 12.134 (12.134)\tLoss 0.2997 (0.2997)\tMAE 0.522 (0.522)\n",
+      "* MAE 0.485\n",
+      "Epoch: [21][0/500]\tTime 18.389 (18.389)\tData 15.488 (15.488)\tLoss 0.1859 (0.1859)\tMAE 0.438 (0.438)\n",
+      "Test: [0/63]\tTime 11.972 (11.972)\tLoss 0.3207 (0.3207)\tMAE 0.510 (0.510)\n",
+      "* MAE 0.459\n",
+      "Epoch: [22][0/500]\tTime 17.904 (17.904)\tData 14.930 (14.930)\tLoss 0.2122 (0.2122)\tMAE 0.387 (0.387)\n",
+      "Test: [0/63]\tTime 12.487 (12.487)\tLoss 0.2262 (0.2262)\tMAE 0.482 (0.482)\n",
+      "* MAE 0.503\n",
+      "Epoch: [23][0/500]\tTime 16.428 (16.428)\tData 13.754 (13.754)\tLoss 0.4746 (0.4746)\tMAE 0.572 (0.572)\n",
+      "Test: [0/63]\tTime 14.973 (14.973)\tLoss 0.1626 (0.1626)\tMAE 0.374 (0.374)\n",
+      "* MAE 0.446\n",
+      "Epoch: [24][0/500]\tTime 22.696 (22.696)\tData 17.323 (17.323)\tLoss 0.0897 (0.0897)\tMAE 0.269 (0.269)\n",
+      "Test: [0/63]\tTime 15.372 (15.372)\tLoss 0.2241 (0.2241)\tMAE 0.407 (0.407)\n",
+      "* MAE 0.465\n",
+      "Epoch: [25][0/500]\tTime 25.602 (25.602)\tData 19.498 (19.498)\tLoss 0.2068 (0.2068)\tMAE 0.469 (0.469)\n",
+      "Test: [0/63]\tTime 12.493 (12.493)\tLoss 0.1294 (0.1294)\tMAE 0.384 (0.384)\n",
+      "* MAE 0.439\n",
+      "Epoch: [26][0/500]\tTime 18.257 (18.257)\tData 15.000 (15.000)\tLoss 0.0932 (0.0932)\tMAE 0.304 (0.304)\n",
+      "Test: [0/63]\tTime 15.212 (15.212)\tLoss 0.1325 (0.1325)\tMAE 0.376 (0.376)\n",
+      "* MAE 0.470\n",
+      "Epoch: [27][0/500]\tTime 17.136 (17.136)\tData 14.089 (14.089)\tLoss 0.3763 (0.3763)\tMAE 0.619 (0.619)\n",
+      "Test: [0/63]\tTime 12.901 (12.901)\tLoss 0.1491 (0.1491)\tMAE 0.445 (0.445)\n",
+      "* MAE 0.524\n",
+      "Epoch: [28][0/500]\tTime 20.938 (20.938)\tData 17.498 (17.498)\tLoss 0.2409 (0.2409)\tMAE 0.533 (0.533)\n",
+      "Test: [0/63]\tTime 10.014 (10.014)\tLoss 0.3095 (0.3095)\tMAE 0.530 (0.530)\n",
+      "* MAE 0.481\n",
+      "Epoch: [29][0/500]\tTime 21.578 (21.578)\tData 18.145 (18.145)\tLoss 0.1196 (0.1196)\tMAE 0.340 (0.340)\n",
+      "Test: [0/63]\tTime 14.748 (14.748)\tLoss 0.1905 (0.1905)\tMAE 0.374 (0.374)\n",
+      "* MAE 0.443\n",
+      "---------Evaluate Model on Test Set---------------\n",
+      "Test: [0/63]\tTime 13.635 (13.635)\tLoss 0.3114 (0.3114)\tMAE 0.513 (0.513)\n",
+      "** MAE 0.449\n",
+      "Training and test evaluation completed!\n",
+      "CPU times: user 1.6 s, sys: 4.56 s, total: 6.16 s\n",
+      "Wall time: 11h 43min 51s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "print('Creating and training the model...', flush=True)\n",
+    "mlband.model.train(config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluating the data sets...\n",
+      "Evaluating the train set...\n",
+      "Saving the predictions to results/test-2/predictions_train.csv\n",
+      "MSE for train set: 0.3749\n",
+      "MAE for train set: 0.3918\n",
+      "Evaluating the val set...\n",
+      "Saving the predictions to results/test-2/predictions_val.csv\n",
+      "MSE for val set: 0.5022\n",
+      "MAE for val set: 0.4450\n",
+      "Evaluating the test set...\n",
+      "Saving the predictions to results/test-2/predictions_test.csv\n",
+      "MSE for test set: 0.5067\n",
+      "MAE for test set: 0.4488\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Evaluating the data sets...', flush=True)\n",
+    "mlband.model.evaluate_model(config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>True_Label</th>\n",
+       "      <th>Prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>mp-9937</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-0.052763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>mp-1172967</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.140506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>mp-982366</td>\n",
+       "      <td>2.5126</td>\n",
+       "      <td>1.816474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>mp-1175038</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.352745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mp-768127</td>\n",
+       "      <td>3.1253</td>\n",
+       "      <td>2.300649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3994</th>\n",
+       "      <td>mp-8399</td>\n",
+       "      <td>3.2859</td>\n",
+       "      <td>1.227919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3995</th>\n",
+       "      <td>mp-1112109</td>\n",
+       "      <td>2.2573</td>\n",
+       "      <td>1.406988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3996</th>\n",
+       "      <td>mp-1021162</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.039211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3997</th>\n",
+       "      <td>mp-1062676</td>\n",
+       "      <td>1.3146</td>\n",
+       "      <td>0.079352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3998</th>\n",
+       "      <td>mp-1187282</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-0.043594</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3999 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              ID  True_Label  Prediction\n",
+       "0        mp-9937      0.0000   -0.052763\n",
+       "1     mp-1172967      0.0000    0.140506\n",
+       "2      mp-982366      2.5126    1.816474\n",
+       "3     mp-1175038      0.0000    0.352745\n",
+       "4      mp-768127      3.1253    2.300649\n",
+       "...          ...         ...         ...\n",
+       "3994     mp-8399      3.2859    1.227919\n",
+       "3995  mp-1112109      2.2573    1.406988\n",
+       "3996  mp-1021162      0.0000    0.039211\n",
+       "3997  mp-1062676      1.3146    0.079352\n",
+       "3998  mp-1187282      0.0000   -0.043594\n",
+       "\n",
+       "[3999 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(config.results_dir / 'predictions_test.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Riljfmz/mGpTHmxPr3SwRx9NfOGN5MJMRdkzBHSMNWlFKNWdhewZ3jTSYaERIAWeuLuM5Jn6sVcBVTs/G7hypk0q9RK7mLAbLLrftruM5FneONFlb9ajkbUq2wR9e/21e+T9fwEljAGLD5GPnvYhPPuoFpIaJAYQJuLZioODQV3RppwohBPUgxjINBssuFc9mvBGSdy0evqGHPzhl1YJu3gcK4zaChNv31KmHCbaVHDP7heVEFigXgePpL5yxPIhTSRin1PyEVpQyWNR/+3asTbtsC/xIMtoI6S24pBJt6Zq3aQUJt+9t0F9yGe7cWKNUIRUMlBym2hGg2NQY4fmfeDOn3Pnb7nnvHNjAO571Zu4ePhU7kahEYRqQs3TyZaji4ZgGtiHYNFBgoOQQRCnbxttIpRgoOjx0Qy8PP8QK51B77PsL4/YXXco565jbLywnskC5CMyotuxfRrE/J+oG9wMZ2zRIpGT7VIuSYxEmkiSV7Jzy2VPzaQQJCrh3vEnFs+np1BqidEKkFSWc1JtHCIFSinYUEyWSu0ebtPyEgWu+zR/+2yfIhT4AUgi+9Ojn8fVnvJyJVGArRYreHZyxYIgSxVQ7phkmSOC0oRKOaXLSmgLVWkiSSqp555A36yPZYz8e9gvLiSxQLgLH0184Y3kQJynjrZg79jboL7goFK0wxTAEhiEIkhTLEIzWQ27cPsnmgRKOaVAPYlKpMAzthtgKE8YaIfeMNRlpBLTu2c7bv/sRHnPnL7vn2lkd4p3PejO/HD6TVaaFg2S6HSMRWBZYQiAVSLSX9uZVRU4ZLDHtJ9y8q84jNvRyxppyd4k8l+bk0eyxH2v7heVEFigXgbnKKGY4kTe4H6hMNEN+uW0KU0ApZxGnEj9OqPkJrmlQ8yMSqXBNg5xtIBTsnG7Tl3doRwmpVFQLNu0wYVctYLodEaWSJ/32R1z8r39P2W90z/Uf5z6D917wF4RunjRK2VMLcCwDwzAoOBYGECUpsZQYQmAISFJJPYhpBAlBlCKEIu+YgJhz3xwO3aqY7bFrsit3ETie/sIZS8v+ibtzhqukUnHL7jrtMMEUir31gFQpCrZJI0wIUxPHNCFKGVMRnm1w5poyqVTctreJIRQbhc9T/uW9PObGa7vnGS328nfPewu/fvCjaTdCBIpqzqQRpiilKLgWjgntWFILUmwTiq5F0bVoBAn3TbSZbses7/cIopRWpP274eB9cyDbY5+HLFAuEsfLXzhjaan7MfeON8lZJu1YsmVVidFGyK4pH6UUfpyiULiurQMPgukgRkrF5sEimwdL/Mm5G2iFCV/7xXZO/fV1PO+f3k1parx7ju+f+Qd8+A/fQCNfxklSTAFRoujxHBIlCJMUP0poKYhShQDcjmSaaxs0/ITJZkgiFZ5pEqeK5ID98QP3zbM99sOTBcpF5IG2wf1AQ3fNTPG7nTWKrtVR7jFJUoltQD2USCUxDQNhCqqezVA1R5IqptoJD15b0Vswnk0lCXjZF9/Hqf/xje7r170SH3rW67nm7PMpOgZmrEtx4kSSKpj0I9JUEacKKQSyIyDT4xnYjk07TkkV2JaBa5vkBDRCvedoHbA/fuC+ebbHfniyQLnIPJA2uBeb5dz+OZPsGK2HFFyLcs4GAXeMNPCjhOHePLfvbeBaFgqFCYy3IsJU0lNwWd+bJ0wkEsj938/Jv+L/0Xffvd3X//Xpj+R9z72ERu8AqZ8QJpJUKqJUESYKHRN1YDQAw0BLriHoLWrRCyn1D9dUPAaLDjunfUYbIWeuKc9qM5xr3zzbYz88D9x3nrGsOJLSlOMRUGedwxDcOdKgESRsHiiQpIqRuo9jCpJUopQgiCVxmhKlqS4dSiWtSO9RT7d1u2DSbvOn3/4EhS/+c9dLO8p5fO+lb+HvT3o8U0GCqgU6GdPRnYwSSNFfm0KQSrBMgeMYhGGCRJAqhWUYBInEFkIHcUOQKCg4JrZp0o7Sw+6bZ3vshycLlBlLzpGUphyPfvoDzxFLyZ5pn5MHihiGQcWzuG1PzN66Tph4lkE9jJFKUHZtJtohtfaMrqqiGQr863/F27/zIdbv2TeLHDvnEfzLX1zKr+0+ksk2jqkDbppCouBAowXT0MrmlmUipEJhIJSkFaZUPJucbZKkijBJkVJScizOP22ATYMlxhrhYffNl8Me+3JeUWSBMuO4cuDFUHLNBZemHGhLcLiAutCL7sDHxUnKL7dNzTrHaCNg+4SPH0kafsJEK8RzTAaLLu0wpR1JplsxedsiRmILA8sGqRQqSXjZj77Ka/73a9gyBUDaDr+/+K384g9fStyMiLZNIZXCNAwQEiEUhtIL7ZlwmypIUoVjmVgGJBJsC2zDpOpZlPMuPXmLME5ZXckz3gw5a6jEUx+0mr6iu6DfxVLusS93QZksUC4yy/muuNTMdTEUchZ7pwOGKrnDlqbU/PiIAupCLroDx2ObgomW7qc+baike6X9iN01n3YYc1sz4LY9Nap5hy2DJfryNlN+wq7JFqZhEKYp9UB72gghWD2ynQ/8+0c4e/ed3XNuXbuZT//FZdzSu57SHePUgphWGJMqRZjoZT4WhNHs350CIqlI44RUGlimoJpzUAKKOYfenM1UOyJn62X2yYPFWZJpC903X4o99pUgKJMFykVkohly5946t400aIcpedfk9FUlThkqL/kfeqk51MWwbbzNfeNNevMOeefg582Upky1owXV+m2baHHb3sa8F91c45lsh9w12qAnb9MIdIC5fW+DVpTQW3JJ6gGTrQjHNrl7rAkoLEPg2CauUsSJ1HqNScJLfvU93vqjz5NLdMRLhcGnz/tjvnrhn9HbW2SsFtKMUvK2QZikxKkiiFOUgrmqcAR6FqkApSQCgTDAs0zW9XjYtmCVnWPjQJHNg8WDJNOWKytFUCYLlIvERDPkqpt28evt07SDmEQpLCH4zbZpHrq+yrPPWfuADZaHuxiGez3uGm1w70SLc/L2QRfDTGmKYv5av8lmyB0j8190PXl7zvHYppYbi1NtdwDQChPKORsDmGqFtKOEdbbHWCMgVTBYdDipN89kO+KesRaDUyN84Ht/z2O23dwd2729a3jns9/Cr4ZOxQwVRjsilZJmkOJHAj9KCVNFks5ebhvs+94Q+/5vGOC5ur/cNrXHzZnrqqzvzTNYzq2oVcxKEZTJAuUioJTiJ3eM8qM7RlHAQMntZg3HGiE/umOUimfzRw9dt2I+wIvJ4S6Gomsx3OOxY6rNlsEixdy+i2H/0pS+vDNvrV+iFFOtkIHS4Zfxu2rBnOOxDK3AYxiwt+7jRymJVIw2Ql2qkygQgslWRDOIQQgc22NDr8dIrc2W73+Xt/7nJylF7e5rfulhz+IfLryIyHVRkSKR2k7Bc0xq7bjTfqiDoOj8mwmU+08spQJL0J1x9uYdhqoeAyWX09eWedj6KskcvnxzbQXN/E2Ww/bQShGUyQLlIlBrR1x39ziJVJzcV0AY+kNXcCzyPSb3TLS47u5xLjhtkGrhgTerPPzFINjYV2BvLWDHlM+GPjFnaUrZs+et9estONTbMTnr8BedH6VzjifvGHiOxZ7pNs0gZqoV4zomnm3imoIW4NkmE60IlMK2TKZbIWNbt/P/vvIhzr/t593X2l3q5y+f/iZ+sfEc8rYgTnUQdCwtYBF0rBgQ+/TB959N6t/M/trhukTItgWuZbJpsMSWVUX68i4376gx0YywDGPWfixw0F5tzjYBRRDLZZE0WSmCMlmgXAS2T/mM1gJWVdxukJxBGIJVZZfRWsD2KX9BgfJESwjNdzE4tsmpQ2WGKjlaYXLI0pT5av1OWVXmdzun573oPMc8aDx1X7/uZDNk21ibnbU2qZT0F10kUG9r6TJTKFpRigIKtqT/+mt4x1Ufo69d657n2w+6gMuf+ErquSJCQTtSGAbYJliW6IxFYlsCEoFlSAKdQ5oVKA30Ult0ltuWIVBK28OetrrEUDnHjimfkUbIyQNFBku57n7s9slWZ5oqunuwY82A6+7Sbo0PW9/D6oq35EmTlSIokwXKRSBJld6TNOa+61mGQaK0KdN8LPcyiaNhIRfDpsEi557UQyNMD3mD6Nb6jeg9zaCjGj5QdtnQW6AnbzNYctk+5R/2oltbybGrOx6DvfWQ3+6YpuZHCAGtKKHdmXU2wja2ITA6UmbCgJJj0R6d4HX//U8895Yfds8x6ZX5q6e+jqtPecy+8wIJIKT+xjQUUimkVEi02o9lmoRJimPqJXaS6mW3TtzoAvO8YyIVGELw0OEqJ/cXuHeiTc2PGSi5lHI2hqHVgXKWwf/ePQ4KHn9KP0IY+v03Yzxbz6In2hGrKrklT5qsFEGZLFAuAv0lh6pnM9mOWF3OHXSBTrYjqp5Nf2mOtO5+rIQyiaNhoReDLuaef4k1s5/XChP2TPnsnm6zc9Knt+DopaVSB5xHe9PkbJNVnb/PlsEi2ydaXH3LXu4abTBWD4lSiVSQsw3Knk3RNrh7ok2cSIYqOeJEEqWKU275Be/67kdZU9/npX3NlkfxV095LeOFnu4xozPOmaAXKxCpIiZFAKnQFrKGMMi7CilBpgrHhjRVOvvd2ZcMYknBMegpONiWyS+3TbNrooVhGpy5ptKRUdP4sSRNtZDw3npIOWejlNI2s56NEjDdirqKQkudNFkOxe7zseBA+Z73vGdBj7vsssuOejArlXVVjwetrfKzreNMtWKKOQvLFCSp0oZOYcp5m/o7jnVzs1LKJI6WxbgY9r+R5CytihNJRbMdE8R69u3HKUJA1bNohQk7JltMNPVMsbfg8rud04zUA/oLDs0oYfuUz2QrQgiBaQocobtjHFMQGALPMoiloh2mWEGbS675PC+4/t+6Y2o4ed7zpFfx7TMvQAnRDeKKfckYs7MPqZQOnrHUx/K2QZRCLFMqeYeya7K7HhIlKbZtgJK4tkXe0ZqXQkAxZ9NbcLp+OzmgGWrtyXInwE21I/bWA1phglRQ9mwc06ThzwRBQSNNZikKLXXSZLkLyiw4UP7mN7/pfp0kCVdffTVbtmxhw4YNbN++nTvvvJOnPe1px2SQyx3DMHjag4YYaQTsmPRJlMQSBomSBLGW13rag4YwDrE0h5VTJnF/uD8Xw+wbSY7b9jaYasVIJO0gYfekz3gz5Iw1ZUzDZLgnz5ZBlxu2TdEvDNZWc3i2yWQ75tZdNcabIc1Qv5aBohEkuJb2vt451SZKJa04xTT0knvLfb/n8u98iJMm9nlp/3zDWfzl09/E3srgrM3FgzZYFJiGbksMOiVAsvMgzxZITDzLxDQESkpSCUXPZLCU58w1FXoKLrsmW9w+0sA2dRG8EIKBksPqao5ECnZO+Zye01qU94y1qPsxedukv+xiIRhphow2A4o5i7xrYptilqLQckiaLGdBmQUHyu9+97vdr1/5ylfy8Y9/nFe96lXdY5/5zGf45S9/OddTHxBsXlXiZY8+iZ/cOcbdIw1acUrZdth8UonHnzLA5lWlwz5/pZRJ3F+O9mLY/0bSjiW7p3xqfoRUkHdNBjue2rumA/KOydYxQTtKEehldiOIuX2kwXQrohEm/H53HcPQLomuZdIUqe6VloqcbWptyShFRiGv/vHXePnPvzXLS/sDj38ZX3rYM5HCmCMyzkYCoTz42JSfsLmvgGEKJtsR4y1FnOqkjWmapBJ21QJMw2CinVDt+O5EKZRcg6Jr0wwlq4ouU+2QVuixc8onTVPKno1AkLf0Xu26So6JZsi2iRarq9rHe0ZRaDklTZYrQik1f4bhAHp6epiYmJg1Q0rTlP7+fqamphZ1gHNRr9epVCrUajXK5fIxP9+RIKVkVy3A79TKra3kDjuTnKHmx/zkjlEKrjVnxrYdJbTChMefOrhs77rHkvFmyI9vH2V1xaMRxnzvd3uQUtGTdxBC91XX/YTNA0WakV5Wrql69HeW9Nsn27SjlLJnESeSX983xUgzYPNgCaX0z6uehWkIploRE62Ioe138bff/TBnjNzTHcdvV2/hkmdcwta+4QWN22B2PeT+CKAvb7G+N8+UHyMMgZSKsmvRU7AJY8WkH+GYBqYhGCi62KbBqUMlbMtgpBYy0Q7pyTs4psEZq8vcMdLo9ISL7lnKnoVjmow0fG7b3WBdj8ejT+5joJMln9knXql74PeHhcaSo7p99Pf3c/XVV/P0pz+9e+wHP/gBfX19R/NyJxSGYTDckz/i562UMonF5EjKoPYvMYpSSRSlFDyLmYcnqcI0BJapD9w33mS8GbK6kmO8GRElkuFej7RToVDIGYimYtt4i0SmTLYjdkwqcrZAxQnPv+7bvPEnX8ZNtVVCbJj842P+hE8/5o8JxexZ//4dNAcyV5CcebwAWlGKH6esKrk0wwRhGPQVXdZUXBIJA5HDPWNtpISSZ4ESWJZBzjJZ36s/Z1EiMdDNDa0wYeNAgfWdz+DOaV/PolPt57N5sMhZ6yoYhmBPzV92SZPlylFddR/4wAd47nOfy4UXXsiGDRvYtm0b1157LV/+8peP+LV83+fBD34w4+PjTE9PH81wTgiOpEziRKizPNIyqP1vJCXXxHVMokiSt3V4akcJPXmHOJHcO9YiTvVzHNOkHiTU/JiRekDJtXBtk5F6yEgtJJKSJFWkCqSE6t69fPh7H+URO3/fPfedfet58zMv4fdDm7G1wM+soDjz9UwS53CB0zjgsVGiGG8E2LYu/6k4ZldUw7EEStmYQpcIjdZDNvYX8DoF9ULAYNlh15TPwzf286A1ZX5x7ySDJZe8o1cdZ3g2rSglSaVWRpeSx586iBBiRX9+jjdHFSif97zncdZZZ/GNb3yD3bt386hHPYqPfvSjnHLKKUf8Wpdddhnr1q1jfHx8/gef4CwkM3wi1FkutAzqwBvC5oFCVxyjkrOp+TFjzQihoJAzKbkWW8dbpFJyymCBvrLHfeMtJpshAmhGKX6UYpmCe8ZaNMO0OyYDxZ/c9H3++oefpRAHAEgEn33Ec/jIH/wZoaVLu2aSMDbQqRHvds/MlAPN+G0faoZpCS2bNlM6VAtS1JTP6nKOkwYKxImi5sfkHQs/SQCBaQg8x8QQgjBOsS2DOJHU2wm2aXD6UJmN/UVGG1FnVTIT/AQFx0J1SqbW9+apeAf31GccnqNex23ZsoV3vetd9+vkv/71r/mv//ovPvrRj/LCF77wkI8Lw5Aw3CdjWq/X79d5jyWH2qNc6CzwcJnhE6HOcqFlUEop7h5rzZJAK3o2/QUHxxSMNUJqQYJnmQihKDo2UaowDRiqeKztK7C2kuOOvXUtfWaAQtCOEvwopRWm3WXxYGOCD139MZ5wz43dcW6vrOKtz3gzNww/aNb4ZUcncv9gKNl3ISWdYxY60x3PsfaWnSBpdMqGHFP73ximIE4U/UWHdpRS9xMmmxGmCavKOR65sY8o1fWQSZBoqbWCQ6XT3rlSirdXIkcVKOM45oorruCrX/0qY2Nj1Go1rr76arZu3cprX/vaBb1GkiS84hWv4BOf+MS8j33/+9/P5ZdffjRDPa7cPdLgf+8aZ9t4Ez+ReJbBhv4iZ60tkyhtCRrGKalS9BRcTl1VZENfYc5yoAMTNidKneVCyqC2jjbZNd0mldBXcLCNlHsnWuy4dxLbEJyyqsTmwYJ+r4DnGAgMmkHCVDtksOzq3xE6CNkIpv2YvG3Q7HT+gA5Sz7jtf7niB5+kGjS74/ja2U/hvef/P9ruwXvNnRrwg/YebROUgCTpLK+FDoQ5A4I5Mt4zy3PLELosxxD4UUI9iDCEoLdgEUuT04tFmpGk7NlsGiwAgnaUJ5EKU8BkO2JDX6G7d70SirdXIkeV9X7zm9/MzTffzDvf+U6e//znMz09zbZt23jWs57FzTffPP8LAB/84Ae5/fbb+fznP8+Pf/xjnvOc5xxyj3KuGeXw8PCyynrfPdLgGzfuYKoVs6bqUnBsWlHMvWNt/Djh7OEehns8xhsRIw0tRFt0LR51ci8P2zD/TPBEyYrvn702jIMDupSS/71rjIrnsLE/z1Qr5t6JJnGq6Cs4jDUjSq5FMWcSJ9r2YKIZ0o4lliGQUvLwDX24tsFP7x7nll01mqHOgs8sexOlYGKK9/zgn3jW7dd1zz1a6OHtT3sDP9r0CGC2WMX+7L+sNgDLgJxjkKbgJxJLgGEITKFNwRKp/8G+QDtjEKal5nJ4jsVYI8AxLQwBAyWHiufQV3DZMJCn6Fjdvu0DZ4lzrSROhH3s48ExzXp/61vf4ne/+x09PT3d0peZwvOFsHXrVj7xiU/MKmI/HK7r4rrL906oL27dlXPqYLEbACo5h7IXsrsWsH28RZKk+Imix7MZKLrsqQXcuG2aVMK5Gw8fLFdSneXhLtL5BDK2TWoh37xjc9P2KUabAUpBX8Fl91RAlCTUgwTPMhhrRbi2yepqjmrO1so+7ZD/vnUPjSCmHiS0goQgSTGBdiIRwBPuuZH3/Oc/sKo52T3vf572ON715Ncw7e27WA41g5hJ2GiRDHBtg7iTdbcFFD2bvGPgmAYKnXybbIW0IwlSB9a8beA6FqsruU6Jkt6X9GwDz7ZYXc1T7ewlFh2Lh67vYbwVLXiWuJyLt1ciRxUolVLk87OXJc1mk1Lp8EXVM1x33XWMjY1x5plnAhBFEfV6naGhIa666irOPffcoxnWkrGrFrBtvMmaqjtrluQnKe1QsrricO94C8sy2NS/b6ndX3Lww4TRRjDvsnm5yFHNN1OZL9lUck0KOYtt422Gez2KrsXM3K3Wjrj+nnF2ToW4VkiYKPwoQQhFrZ1gGPqRtilAWLQ7pTVVz6bkWJiGQb0V8bud08SpoOAY3SVyrMALfd5x7b/wopuu7o53Olfksie9hqvOePyCfwcGYLJPRDdnGRiGlj9rxylBlBJEiZY1s0zyHXvbOI2wDEXOtujJOwz35hks5XBtwa4pn5xlMlhyWVP1OHu4StlzyNsGO6cDxlvRvKIhGceOowqU559/Ppdeeikf+tCHusc++MEP8qQnPWlBz3/hC1/IU5/61O73P//5z7nooou46aabVmQtph+l+IlkjTP7Dp6mikQqHFN3jeSs2fWRjmnSkFpBe772xOVQZzlfEJwv2bRloMh4M2TraIPf76pz47ZJNvUXOHWoRKrgp3ePMVIL6czV6CvY7Eok9XZImCo9Q1OKvGMSJnqpnUhJIiW1IGasETLpJyRSoJTEj0EqSSLhYdtv4UP/eSXrayPd9/OTjQ/lbU97AyOl/gX/DgTgWqCkVhICLc5hmoJUamFebQkBQkjyjkkQp0RJSsWzyNsmOctCmIL+ok3OMQjjlCk/pidvEyeSvqLLUHnfPvRM+2qj47aYcfw5qqvqyiuv5NnPfjYDAwPU63XWrl3L+vXr+Y//+I8FPd/zPDxvn0BEb28vQgiGhoaOZjhLjueYeJZBK4qp5PYpBJmmwDIEzTDGEIK8M3u2pz2gBQXXou7Hh102L3VGc74g+IgNPZ0gGjJYclFaDBzPNunxbG7f2+CGe8ZJUsW0HzPZimmGMXeNNPjJXWOsruQYrQcYhtGpFYVEKoIoIZXa0zpMUnKWQc1PiNIUqyN/dt9Yi2rRIU0UQaJFMRS69jCXpLzyh1/kol98F6OzmG7bLldc8HK+cfZTSY/g9yWAnCX0Pqeh1X1SvZrGQxEiCWPdqy2AKFZEtqTqOeRdCxM98w+kIgwSbtvT4KT+AlGiEErva+Ysk+He/Ky/43LaVnmgclSBcmBggOuvv55f/epX3HfffQwPD/OIRzxiQa16c/GEJzxhRRebr63k2NBf5Pd76pQG7e7y27NM8q7BPRMR1bzT1QIErSRT9xMGyy6mwYKWzUuV0VxIxv2nd4+zdbRBkCj2TPtYpsAxTYSAIE64fU+DezvF4r0Fl7VVjyC2uGe8Ta0d4zkmttnpXw4S4iSmFSWgQKK1Pv1YIZCEiSRVelxuRzEoqgcYwkCgUEoRpXDmyF188KqPsGVsW/e9/HLtGbzlGW9mR89qrM5GY3qI930gBvqGJQxwhEE7Smc9N+7ItAnRUSO3DFaVc6yqeNTbIRPtGCkBJIYBY82QVCnWVvOU8zauZXL6mjJD5dmmYMtBsOKBzlEFyosvvphPfvKTPPzhD+fhD3949/jrXvc6Pv7xjy/a4FYKhmHwB1v62VP3uWO0OSvrXfdT+gs2fcUcE62Y1ZZJLCV1PyHfqbOcaMULXjYvhRzVfCU9jiH4xT2T+FHK5sEirm1Qa0fcvGsaQ8D6Ho92HJMkKbFp6Ay0UgSxouhamAL2TAekSjJQ0D+LpUKmEtc2aCcGUZoipSJGIQyB2RFBti2QUhDFEqlSlFKkccprr/8Wb/zZ17te2qFp8dHHvYTPPOKPkIa+YSVyttXC4bAEuJahayNThULOcktsJzOBFBxT/z+VWlhjz7RPlEqUgnLeJu+YxKmkFSRUchbrez0Qug3zlMEi+4/qRG1fXWkcVXlQuVyes+i7r6+PiYmJRRnY4Viuohiz6ijjFGEI1vV4PHhtlShO+b97J2kGCT15m/6yy0DBJZJq2QsSHL6kR3HL7hq37qrTm7fpK7m4psG2iZY25VJ6j3bbWJMgTVld8UiUnm1HqZa0nfIjploRtmHQW3BQSqvpRGnKhp48jTBmrB7RCGK9zLW1SESQKCxD10q2gpRAwqaJnXz0ex/l7D37vLR/P7iRNz/zLdwxcNJR/w4MOnuTSheMO5ZBEElSZrcwmoBlaiVyhKC3YNOOUkquRd4xKbg2AyWXZpTQDhMs06Dq2Tz1QUPEqTqiEqCM+88xKQ+66qqrAK0U9B//8R/sH2O3bt1KpVI5yuGeGGxeVeLkgQK37q5z+94GzSCm4JjUO3L9zz57NSONkMlWhCUEhiFYX/WWfSHwTMbdj3U7XSK1n3Xe0Vne8XrIQMmhmneo+wmmgHvH2yRSal3HMGGqFVPK6T5rWypaUaItEZS2ac1bJp5rd4KQIG8LmoHUrXyuiWNp/cSZfU8EhElCkiiCBJJU8tJff493/PgLeImuuU2FwScf9QI+dt6fEJv3LwkiAV/rY2CCTiId8JiZ/m7Z2RaY8TiLEolvaJ8d0zSYakeEiaSat9nQm6cR6jrbimdnheLLlCMKlG984xsBCIKAN7zhDd3jhmGwatUqPvaxjy3u6FYgU+2YndM+CljfV+jODHZM+ZRyFo/Y0INtmUe9bF6KQuJyziJnm/xy2xSWIUhThWUKevIupZyhu0N6CwwUHcbqIfeMtRhvhriWIIwl035MnKTUfEW9HVMp2N2A4ocpSZoihKCUM7FMQTtMMYTAsvTM0jRd+os5UuUTJ4og1j7YSioUMDg9xgf/6+957Lbfdsd8T88a3vKMS/jN2tOO6e9mf7otjQoMEwQ6q5RIRSuMyTtmpxxKJwAtw2BvI8RA0AwTNvYXeOTG3qxQfBlyRIHy3nvvBeCP//iP+eY3v3lMBrSSWUjS4+6xFo/c2HtUH/6lEsSYbEVMtyO9VDQMeos2Qmkl8IlWRCuMcS2TRhAz2ghoBBHtKCFIdHtef1G3Gu6Y8rlvsslJFLAtg7TjmZ2kKaZh4EcpdsdgK0oVrmlgCEHVc+gr2AihmGpHNAMw0pQUxR/e/EMu+59PzfLS/sJDn8kHH/8yfCd36Dd1P7BNXcsZJ6o7i5yRTdNxUPduS9FJOJkCifbhMQ2DvGNgCoNWHLN7zKecs7ll5zTtKM1mj8uUoy4PmpycpLe3t3tsamoK3/dZs2bNog1upXFg0kMpRTtKu0vV3vz89ZKHYqkEMWaCP0Jw3qY+dtX0srAVpQRxQiPQwrKWocucDASmqTO0BdfS+3amgWVAOacN2O4Zb7G64hHGCX6UdIq1BZYFQSSJpKLgWgwUXGxL4NoGexvaS7sdSZphQrUxzRXf/zhPvuv/umPdU+rnrU9/Ez876ZxF/z3sTyJBJdqCdmbPEvTyPJU6kBpC4Nomw70FUimpdf5unmPR8GP21tv4HbNv29Rq7L/fXWfXdJvHbxmgv3RsgnzG0XFUgfK5z30un/70p2cFym3btvGa17yG66+/ftEGt9LYv82w7sfsnPK10ktnqVr1HCxTHHE93FIKYuwf/POOSauTxa37epaZJLKzZ6gYb0YEqcQxIO9aNMIEAVRyNvmcxXBPTu9JSkUziAlTSd41daF2Iml3pmamEERRwpTQQbLlR9ruVwkMFOfffj3v/f7H6d/PS/s7Z57Puy98FfVccVHf/1wkHUtZg30927LzJzWFwHMsUqVVgFZVXHZNBVr8BNhbC5j2Y6RSWlfTNmiFkp9tnWC4xyOWugvp2WevzoLlMuKoAuXtt9/O2WefPevY2WefzW233bYog1qpzCQ9xpoB2ybatKKUas7C9rR24K5pHyGgESRdi4KFsJTGYzPB3zZSfnXfJDdum6Lhx5imIEokPXmb6XZMf9GllDOJJ/XsyTW1Po5rmdgmtCJJKhWrKzk8x2C0HmFbBgNFh13TPkEsac8Ul5va0CtIIxxTMNVOSBVUwhaXXfNpnnfLtd3xTXhl/uopr+P7pz7m0G/iGDGjIiTlPqELx9IZ+Rk18XLOYsQU9HecExtBRN42yNkmBceiHaeAxEBrVPYXHe4abfCTu2yecMpAtgxfJhxVoKxWq4yMjLBq1arusZGREYrFY383X86Ucxb9RYcf/H6UVEoGSy6OpYuuHWufcvVIzeekvvyCZ39LKYhhmwZhIrll9zS/21UnjCWrKjmiJGV3LWCsHlLwLF08X3LZ1J9nz5RPM+joRdoGjUDvNapORnu0oXu4FTogtiJtTai1HnUWPEkVtglpqgPIY+67ib/7r39gbWOfl/YP5vDSPl7MJG4UOgsOYFkCw4AgkVRtneEXwqCcs7lztEk5Z9FTdMlZ2uN9uh0xFcQMFnP0FRxaYcJQKUfZtai14xUhm/dA4agC5bOe9SwuuugiPvOZz7B27Vp27drFq1/9ap797Gcv9vhWFJOtiJG6z10jdWKpGK0HDBRdKgWHJNX7bht684w2wiOa/S2lIEbJ1b3Kd420SFLJQNHp2pxaQlALY9qJJGe22F1r4YeS8WZImOoZZJykOiBKcGxBJWfTjiVhkpKmECQJKF0qpZQWmohTpVsAU7CjgL/5yRe56MZ97bENx+PyC1/Fvz7oibAEQcQE3I7+JEDBsYikzlLbpklv3mZdT55UKnK2nlkGu2u0Yp3db0YxdgDjrYiiowUybEvQjlX3bzlQcle8PfGJxFFdWe9///vJ5XIMDw+Tz+dZv349tm3zwQ9+cLHHt2KYaIZce9sIv9le7yzFFGP1gFt217htd52ia3HaUImBUo4okUc0+5sRxJhoRRzYHzDTuTFYzh2Tzo1GmKLQIrGp1DO+ME4ZbwRMtGL8WJIkKTunfbaOttg+5SOVRHa8aBKlA18qFUEkmWhFREmK6Mweo0iRSkkQprQTRTuGSEKk4EG77uA/v/DGWUHy5+vP4ql/8Qn+9cEXLkmQFIBjQc41cSyTYs7uqowXXZsNvR7re/MUcxatIKHk2RRy+gY5WHDwbIMwleyeDnAtgzUVj5xjECf69zDRisg5Jj2edcSfk4xjx1FdWaVSie985zuMjIywfft2NmzYwODg4GKPbcWglOLGbZP8blcNIRRlz8a1O/tscUqaKpTSKtXt6Mhnf0spiBGnElMYrK54pErR8BPqQUwrSsnbWm+xEcS0Y+2hXbANAikwhIFlSpK0U1eI/henijjVHTWyIyih0n2K4Qqw05jX/+z/47X/d7CX9hcf9kyUWLqe5568yVAxR8Gz2FvThfbre/OMNSMaQcJwb55mmOKH2tdGSkUzSjmpv8hJfYod0z6ryjm2jjeRCqRQtMOE7VM+aaoDY8ExuXlXjYrnZP3dy4T7NQVZtWrVrH3KByp1P+Z3O2vEicI0FLV2RCNIKboGpbwNCO4db3P66jJTfnJUfbvHQxBjrmJ22zTwHIOyZ7FGedw70dRKOKbANU1qQYwRpchUi1G0Uy2OaxgQxR2RW6HFagE6FTGkcl9vdLLfJPmUsfv46Peu5EEjW7vHblq9hbccgZf2scIA+osuPaUcqZR679m2ME0DxzSpeoJGmCBTRYrCMw321kPKOYveghYWbkYpzTBhddkjkZJdUwHTfoRpGGweLLCxr4BhwO17m6yuukw2g+NedJ6pox/Mgq/Wxz72sfz0pz8F4CEPecghf3G//vWvF2dkK4iJdsTuaZ9mmFBrRzSjhGaYUA/Ba0eUPZswSbhztMnJA8Wjnv3NJYhRck0aYcp4M8QyBAKIpTrsB/zAC6Hkmmyb9LlzpN5tr3Rtk8Fyjs0DBTb0Fbh3oo1hCAQCIbTEWERKM0g6ftq6qDDszBYTpbrZjhSdlJmJh/tbuZpC1yIaMuXlv/x33nrdl2Z5aX/sMX/CJx/9x6TG3ImsY8VcNhAmIJTusplux5iGoNaO8MME1zZZVXYIE7Btwfi0T9sysW0LxxBsHWvRk3dZW80x2giJ4pT1ZY9pP0YIhzPWlFlVzlFrR9w31iZJFXumQ77z612ct7mfLatKxyUDfiK4fB4LFhwoL7744u7Xb3rTm47FWFYuSneYjHTMw+KOYG8qJe1AtzWWPZsez+YRG3ru1wduf4l/XYReZ7QeMNmKmGyF2jah6NBbcOf8gE80Q+4cabBtooXfKdnx44TdtYA0VfsEO0xjluDuyf15fj4dEMSSME4Yb0aESUoiwbMFtml2DbX0e5/dtTIXEu2RPTy9lw9/70oeufPW7s9mvLRvHdp81L+rI8G1IEr2BUgttrHP70YpHQBNUwvtgsKxrI6KuUHFs6n5Osvf47m0ghTLNNg8kMe1LeJEMtIIaAQGJc/mD04b5KS+PI6tRYj9KGXnpM9II8A2DTYNFMg5Bk0/4Y6RBlN+fMyFMU4El89jxYID5Z/+6Z92v37pS196TAazUjENwVQzYqyh+5tdy8S1DKIEmlFCFGo/lXaUcPdYS9c+3s8P3P4fascUTLX0ch/AMGKqnnPQB3yiGXLt7SNsHWshpSLsBMixRkjRsXj4Sb04tsF4I6Idppy6qkg9SLhnvEXVs1FSsrfu44cJcacdxe7UDKZKIpUgSVISqYUz5k1DKMUf//b7vGseL+1jjY0uFDeF0jqXdCTYBNDZNkgUOKZBX8HFc0zW9xYoeZa2zfUTwjjFsw1aQUo7apN3LISAO0ZarO/NU807lHOK+yZ8hg3B2Wsr0GnPHCrn8OOU2/Y2EEKwtprDMDqlUyJlsOTSCJJjWi50orh8HisWHCi/9KUvLehxf/7nf37Ug1mpxElKK9ZF0WlHGSdViqTTCww6w5uzjQXdnefbI9r/Q722kuP2vdoed201BwjGmiET7YjTh0rsnNZ+PD15mxu3TfG7nbWu6viuKNFK5EArSthT8zllVYmBostYM2RXLaCvYPN/90zQV7BBQG/eZgJF4ifadTBVhInuwDENMISBa0IkDzWP1Aw0J/nQf/8D5+/npb2jsoq3zOGlfSwRgGGBkkrPhvdrRwxTbTdrOSZF02Bjf4GBco6ResBJ/QVOHih2WlQlE42QO0brTJoJ0+2QzYNFEILtk23uGW8yWHIp52xO7s/rJE1HWmim7MuPU6bbESVvn83HjAK+bZn0WeYxLRdayqaGlcCCA+WVV1456/tbbrmFQqHA6tWr2bNnD+12mwc96EEPyEA52oxJpcIxIUh0hjvtzKgEAsfSNYJBAicPeIe9Oy9kj2j/D7UfS6baIdX9gmnZs5huRbRj2f2A75z2uWXXNJYhKHkWzUhfmEXXohUmJFKxbaJNb8GlmLMo5SymmiF+lFL3IwSKkUaIAmrtpOtAaHfKhmJ0W19vXhDEh59LPvO2/+Vvf/BP9ASN7rEZL+3WHF7axxIDvX8qO3ulByHANQVDZZdTh0o4psF942321H1OHih0bDwi7hhpsHMqwBAw0YwYqYes7y1w1toKu6YDegsOZ6wu4dkme2oBcar/NjOqTFGScu9Yi4pnU/YS+osO7ShlsOxScEyk5JjaQawkl8+lYMGBcn9r2Xe961084xnP4N3vfjeWZRHHMe95z3uOyQBXAq4ptH9KCp4lSJRJFEssQweTJAVpgm0c/u680D2i/T/UjTDRXSzevjISxzRppNrLuuTaTLUixhsRu6Z9TEMwPRrTjhLGGiHlnI0fpYSJ1o3MuwYVz6XoWrpgvB7QCBK2T/rsnmwx7SckUgdIDP2eDQFGZ8la91PSQ0wmK36Dv/2ff+LZt/1v99iBXtrHmxlZNNGxcLA79610xtLBFOQsg5xt4UcJ25oRqZLctqtG3rFYVXLZOt5m51SbvGshU4nZ2QoBWN9bYKDkEEQpCEHQufnZpjFLlSmV2jTNEIKxRsDemg7E66oeIAiS5JjaQSwXl8/lylGVB33qU59iz549WJZ+um3bXHbZZaxZs4a//du/XdQBrgRytqn1Bw1wbBOjk8yxDd2ymKpU/6yz3Jrr7nwke0T7f6gtQ2AaUG/HOtOMwDK0CIdlGt0PeCuMGamHFByTasHBNh3GmxE7ptpM+zFpZ5kfxIpKTquaNwLtW9NfdAnChFYsaXZmi5GiazZjKGbZws7FE7b+ig9e/bF5vbSPNzPjNjoqPpYpiBNJ2pEuV1Lom0mSsqce0l9wGSy6jDRC9kz53DfW0sK8UUKYSBpBDErQChMd9JyQ4R6PRpoQJymNMGV9b56Sa3LDffWuKtPOaZ9bdyVMtSIqnoU0DYquRdmzj4sdxHJw+VzOHNW79jyPX//617P8t2+66SZyuQem2knONujxtEf3ZDPqdqQY6H071zEouTaOpT98c92dj2SPaP8PdckxGWtE3D3aQKLQUhSCzYNF6u2IaT9hQ5+H3/GZBp2ACTrFze0o7dQ9CkxT0I4Txps+SsFEI0JYBq4hGGmGNIMDNb01h1uM5SOfv/7Rv/DiA7y0L33Sa/iPI/DSPi4ocC0TncORnay3YqodI4Sgv6A9ty1D96enCnZO+yRKEkYSy1BUPBvXshhtBDTjFCUUFc9EKsW2yTY9eYdV5RyNWapMFpW8Q1/R5bY9daJEUfZ06+h4M8KP02PusrnULp/LnaMKlG9729t48pOfzEte8hI2bNjAtm3b+OpXv/qAnE0CCMMg71jaqhRtAWB0RB5SBXGiVXSEMA55dz7SPaKhssute2r87K5xxhsBqezoIyII45Rbdk5z92iTDb0FEpnS8GM29hXYMaWX8e1Q27oKIRCdsp6+vItMJXePhdimoOiaICW37KnTDGPmyc8cxMN33spHvnclG6b3do8djZf28UAqtGp6EGMIg1RCIMG2tCVu3tXlT2ONgIJrc8bqClJKbt1TI4pSKnmbMJUkUhEHMbYpmPZj7htLSVJFT8HBtQxylsnvdk7j2iaTrZC+wkxCTzBU9sjbFjunfCZbIWPNiNV+zObB4nGpY1wql8+VwFEFyte97nWcccYZfO1rX+NHP/oRa9as4V//9V954hOfuNjjWxFUXZN2oI2iiq6FH6ckSiIlWIbR6WVOMFHsmGpjmQZ9ncLxmYLxehCTSIkfJxTcg7OKM7PQRpBw92iTkZrPjrEmOyabKAXFnEWSapuEIE5px5JcolhVSphux+yc1K1zQ2Vd8DzdjhHo2aVpmKSpJJGSdpBidhwHC7ZFK0ppBXoftCuZMw9uEvHm677CK2/Y56XdsnO894L/x9fOfuqS9GjPR7c6QYEy9M1IdNwUlVLYhkEYJ+yalqzrMThlVZEwSenb6eA7el9vfLzFeDPSlg+ORcm1mGhqBfgHrS1z9nAVz7YIkpSdUz7bJ32qnsOqyj6P+7Jnc3rOYqLlsMbP8QenDszaijnWLIXL50rgqDccLrjgAi644ILFHMuKZU8jJFaKnGMSRRFRAqnSy2BEimVZBKnkzrEWfQUX11L8dsc0YaI9ql3LwDEFu6Z87p1o85B1VSp5B1C0opQ4SRlthKwu57h9T51GqAubLdOk7NnUg4R2pOv4mmFCJBVDZRfXMpDAlB8TJZ3WuYpHX8GmHSWYQnRlwib9mFacIBXkLEGSSv28WFJwTVqRRKn5M55njNzDR//zI5w2frCX9vae1cfmD7CIJFJfFI5l4DmdvToBjm0QJoqevEk5Z2KZRqc9U9Dw9Y2klLOoGFqlfbodIRXkHZNqXhuvFVwLEOQdi80DBXbXfG7b22Cw7CIO6F/3Y8mmwdJxDZIz7N/UkKE56kD5+c9/nq997WuMjIxw880385Of/ISRkRH++I//eDHHtyJohikoRcuPqEezp1x+CiU7IZd3UFJRcE36iy5RLLlnvMZoI2Sw5PDgNRU2DRS5cfsUP9s6zqmDJfxUOxxOtWMKrkkjSMg7WoWoHiTUw5iGn2Cbglak2xibYYIlBOMq7CSCLFYVXZp+zFQ7oqdgc+pQiVaYYpoGOyfb3LRzmjRVJKkkUYpWqLq9zACuaYKSh92LNGXKq//vX3nTz752WC/t5cxMODIMraxeydusKruEsaScs6nmHaSSnYJ0ragUplIn7KSi5GoNysBJ8aOUdpRimwYnDxSotbWQSKGTUTYMg9OHSty8s8Zdo03W9eSzPcFlzFHl+t/73vdy5ZVX8sIXvpDt27cDsHr1av7u7/5uUQe3Uig4JuPNoBskjf3+ATRimGiFWKZguMdDKsVdYw3aUcqpgwUSqbhnokXOMXnIcBWB4mf3jHPn3gYSOGVViS2rSuya9hlvBjSCWCcUQr3Ervkxe6YDRmshQawLoFtRSs2PqQUxcSpZ35tHAbunA6RUDFR0N0gzTIgSSZCkBKns9F7rXm4/1kGzESRE6aHf/8kTO/nXr7yNv7zuy90geevgyTz7pX/Ppx75/GUbJDuNN/v+XkL/v5oz2NSf50GrK2zsK9JfzFHIWcSppBUlRLFkshVw91gT1zIYLLlYhkCikzxKCXoLDr0FB8sQeLal21oPqEEcKOZY35tnqJKjFeqC/1aoRVMeyO2Cy5GjmlF+9rOf5brrrmPdunX85V/+JQCbN29m69at8zzzxMQzJPVORtgRHUHXzsRSdEQhWkGKZyl+v6fOjokW94y1yTsGzSAiSWHbmN7fKjgmYaIwheBRJ/dR8bRXzUy2O04VO6d91vd4yE6f9nQ7QkpdEmQanV7rVJGaiiiRjDUC1vXkydsmRUdnUy0haIUpzSDGtgQKg1SmtMK06yhoCYiAUM59RxVK8me//h7vPEZe2ovJXNurBwpf5ExwbZON/SVKeYdiziRRur5xTdVj51SLPdMhcaLw45QgSWmFWhSkGSRUPZu+oottGcRpSs62mCaiFabkbKMreDxDkKT0FhweubEPIUS2J7iMOapA2Wq1WL1a7zfN/EHjOMZ1H5h3wOvvq3c1F6NDqECkwLW3T7C66uHHOnnTjAT3TrTJ2Sb9RYdKzkIqGK37JBLqfkLF0/3OliGwTQPDgOlWRE/eIVGq6wJoCoESEkMIIgmpkqhUa0XetKPG1rE2SsHDN1TxbBPbMlnX43HPaF0LOBhKJy4Q2KZCCEPPgDrV4wcuu1fXx/i7ZeClvRAEerZ4YCF8t4YS3RhQzFm4lkWQKpJWRN2PyTsWa3pyKCQjjZDBco5HntTDdEeXsxkm5CwTCoqRRkQrSlhV8Rgqu5Q8C0soRhsBD15XoeDsm1nvX/1Q8ewsMC5zjmrp/ahHPYpPfOITs4597nOf47zzzlvQ88Mw5BWveAUbN26kVCpx2mmn8bnPfe5ohrIsaHfWpfP9MrdNtpFSUXJtCjmTROrC9FYY0wq12kyUSJqRrp+74d5xfrN9ktv2NIiTFM+xmGzGNANt85omkpJrknd06YoSBnEKKpUd6TNBmKS0o4SJVohtCap5i+vvmeT/7pmg6FqcPFDCc0yk1LNPlCSR+nlBPIewhVI895Zr+f6/vHZWkPzCQ5/J0y/62LIIkqYAzwbPhJIrKOcsyjkTZ45YZACuJfBcg0RCJe+wppIjZxs0o5TJVkjLj7ljb5OSa/O0M1dhWxYSOLm/wPq+PPUwwTQMyjmTZiSZaumW1qlWQsnTKk6GMGhHKVIq2lHCzmk/24dcQRy1r/eFF17IF7/4RZrNJo95zGMYGRnhmmuuWdDzkyRh9erVXHPNNZx88sn84he/4GlPexrr1q3jyU9+8tEMaUk5eUD3J89djr2PdhCTpJKegoPTNmkGIZ4FjYhOsEzYNe3TCBJ6CjaOaWAguGukzk3bU1xbMNGMCBPdJpcqCBM9pXQtk6Kls95JomeHSap9xcs5gzVVj/6Cw293NojSFCkhSWpEie7zlp2lnyG05mKYHDwx7m3XeN/3P85T79xnSby71M9fHgcv7cNx4LLaMWFtyaERgx8n9OYtHajCNqbat+R2bCi5NiXPphnESGBV2aWv6OLaJr0FffMo5mz6Si4PWluh6Dpsm5wk1/HitoVBkqRMRynDPR7lnGSiHXHb3gbVvM0zz1rN2et6GG9FWW3iCuaoAuXQ0BC33nor//Vf/8V9993H8PAwz3zmMykUCgt6fqFQmNUb/qhHPYrzzz+fn/70p3MGyjAMCcOw+329Xj+aYR8zzlpdXFB9oRBw17iWWQtjvb/VCBS2KWiGcNdogyjR7o2uZeLHkm2TbfbUfGpBQq9nU/AsVuUdptoRU+2IZhCTCvAsk2LOxI8SfKmdDBFgoXAsk3WVHM0oZedkm5MHC1ptO0yoBwlKKYIwRSldHmMIddDbefKd1/O+A7y0v/2gC7j8ia88Ll7ah2Jm2YzYZxsrBDQjhRBCW8faFlXPJkp1vWMzSPTeo20iUSSpIu9a5CyTR2/uY101j9kRQW4ECRPNEMc0GCh67K757JhsYwgtJTfd1jLuBdfUraWmgURw+lCJSt5mbTXPpsEimyCrTVzBHHGglFLS399PvV7n+c9//qIMIggCbrjhhlmal/vz/ve/n8svv3xRznUsuHPMX1DXiucaRLHkzpEGjmXi2QamoRfsrShh+0SLzYNa5mzndJu9tQDHMrGEoL9o0whSLEtQLOc4dVWR8UbAZNvAFeDHKbWpmCBOtKqPqYundfeP5Lc7pukpupgmgJ4NaRUbPcVKJN22y/2tGUphi7+55tM8/0Av7ae+ju+fcvy9tA9EofceZ0Q5Zny2c45J3rFoBAlBlOCWXSp5B8sUDFU8QM+2m0GMEAbFnO6BX1fNd/eFATzbohHq3+lYM+CesRbNMKUnb2NbBuPNECkEphCsrnrkLJMoTXnExl6EEF3HzQdqj/SJwhH/9QzDYPPmzUxOTi6KX45Sipe//OVs2bKF5z73uXM+5p3vfCeXXHJJ9/t6vc7w8NL6p+zPnXvrHKZ6posfSkxD7wFWPJuc42IAEoVr6Vq8iUaEAEbqEYkEM0mxXZskUdry1TTYMemzpxEw2YpxDEHetSi4kpFaRFvtK3uRSnvUNKMUP4yohwl522D7RIt6kBBEKY5tYCEouAbtSBLttyn56G2/5cPf+/tl46U9FzMZesMAxxA41kxLqclgKUfBTZhsR6yteqyp5qn5EYYQeI5BzU8YKDnU/YRSzmJNNT9nZrqSsyi4Fr/cNoVKJWuq2sIhJwAEtqHbR8M4xTR0IC64VlcabbQecNueKLNXWMEc1W3u1a9+Nc973vN4+9vfzvDwMIax78N11llnLfh1lFK85jWv4Y477uCaa66Z9Tr747russ6o7621F/S4IAbbklQ8C8818SPJeDMikRLHFMSpIkgCojTFFIpq0aUZxVTzNtN+jGPpZIMlBPeMt/CjhIJrsabqMdkKkSrAtQRRorpL0CTVdX1xIhltRBQcg2k/IZUS2zII/JQwTnBti5wNcShx44C3H+ClXXfyvOfCVy6Zl/Zc7L83aRp6Fi0wsA0DwzAwTcFAyaUVapX2B62rYBouUZoy0YgwDcHqskdfUTLdTlhdzR0yM72q5PDzrRMoBZW8jR8lTLW1XF3RNenJu+yuBZw6VJoljRYmkpt3TZNKMnuFFcxRBcrXv/71APzhH/7hrONCCNJ0IXMr/SF87Wtfyw033MC1115LpVI5mqEsC1IWFjhStH3tjLhrzjJJpUQpXb9nGYp2LDvLeEFPwaYVJYzUA6RSDJW9Tp2lJJKSgmujgIpn41laPV0qhS/VvhmuUigUSccath5KtOECWEJimVpQOEwSXAseNnIHH7rqo5w8uas77p9tOIu/fPqb2F1ePpbEAr03aQodLOMEpFLYpsJSiiBKGakHDJRynLa6xGRTm4AZQmAKgzPXVVhbyZF3bUYbAXvrQTczPVeHjALW93rEUlFvxxRzNlIpwlgilTZUK7gWm/qLlD0HpRTjTd0AEKeSVeWcVpMXmb3CSuSoAqWUen22a9cudu3axbp161izZs0RvcbrXvc6fvazn/HDH/6Qnp7lsYw7WlaXF+btkjPAsw1yjomBoB2nlHM2YZKSt01iU5EonbH245SJVohpCPZMBQz35qjm7W4iaMbDQUq4c6TORDNmqhUTy32zLL2s1wrkB5b5KLR2ZNxJ1RtpzGuu+wavuf6bWMvMS3t/bAFWx/QrlXoWKTt6mEqCaeja0mYYM+nHKAUbe3vpL+YYquY4Z22VXTWf6XZMmCpyUnLmmgqP3zJw2Mx0zY/pLbgUXBOF7oXXheYxt+1t0AwTio7V8W5PmGhFBLFkd83HEIK9tQDLFPTkXdb1eJQ9+wFvr7CSOKpAuXfvXl784hfz4x//GNd1CcOQxz/+8Xz1q1/tFqIfjm3btvHJT34S13XZsGFD9/hLXvIS/vmf//lohrSkLHTppIQW+fUsgw39ebZNtLXpvVR4toHbEUxoBNoOdbodU/UslFJYmJhC0A5jdkz61P24k7mOmWonerkpZiffZec/80lZzOWl/dvVW7hkGXhp74/W2tQrF8eEGIVjGXoJrqSeNSsIEgmpfvxUO+I3O6d5xEk9hFHKaDNESp35MQ0o5mw2DxToL+XYdBivov01QNdVPUSnZ7vo2ni2xW92TuN07Gtd26TaKTlqdHzcXdvoOjE2w5jThsoUXesBba+wkjiqQHnRRRcxPDzMnj17GBwcZHR0lLe//e1cdNFFXH311fM+f8OGDag5DUpWJkou/IOuFEwHKdsn2oSxJJYppmEQJOBY2oTMs0168w5hklLKOXrvMk24b6LNVCtECBjsiDWIUCeBUDqRINJ9pT2CwwdJQ6b8v2XkpX04bDr2tgKKroVtCBphSiJTbTMr9GzdMhSGaSClLruKEsmOqTYCcB2TkWbIOeuqnDxQJEi0kMhP7hrjrLVVBsu5juwdREnKzumYnGXgWCblzhJ8LmHbephw9roKZ6wuU8zZWIbg9j11TEMwWHIQhlYZcm2TQctgtBmxc8pnQ5/XFXCez1AuY2k5qkD585//nJGRka6i+eDgIJ/85CcXNJs8EblzZGHJHBPoLbo0w0SX87RjhIDegkmqJDVfy67lLIO0cyMxTSg4euYhiVAKejwbQwgmmxFTHeMvwzC0eC9dh4bDlnYuBy/thWKgs9qmAZWcTc4xkakiZ0Ez1MvwvAVVz6Ydp4hOS2ecKixDzz4bYYxpGljAtok2nq0/+g0/Yet4k7tGmqzr8YgSLV4x1fGy8VyL4R5dC7llsLggYduaHzPaCFnX45FIxWg9xC2aXaHkas5isq1FUk4dKhEnKb+4t55lxZcxRxUoN2/ezI4dO9iyZUv32I4dO9i0adOiDWwlMVZfWKCMUj1TKTomYZKSSNW5eGBv3SeMtQybgSKViqDTWTPcm2e0EXLPWBPPMagHCVN+TCOKUVJ1S2RSqVBCC3EcMkgqxYvm8NL+zLl/xEcf95Lj5qV9JNimFhIGwBA0/JiwI3MmBDgGCNPAT1LcjviE6iR0pFK0IzBEStlT2KZJK0q5Y28DqSTtWDJQcmj4CfeOt7vBarDk0lO08SPJruk2YSK7WepHbuw97Oxvn1q9y7qqRzNIGGuGlD0LxzRJUYw1Qtb35ekvOPxy29S8hnIZS8tRBcrnPe95PPWpT+Xiiy9m/fr1bNu2jU996lP8xV/8BVdddVX3cc9+9rMXbaDLmeBQjloHEAO1IMY2DC3qapukKiWIJQYGlkiQUlHzEwwDSjkb1zI7Pdu6m8QyBLYhaAcxMtUWEZFUxKnOnnOYIDmXl/b2yireepy9tOdjpg50prZUOyQKevI2iZSEStdMSr1hiWlotaRISQq2Rd4xGWuESKWQSmeje/I2SayX4et789w73qDiuazrySEV3DfeppKzqeQstk/5KKDsOpRdGGuGSCWp+zF3jTZ55MbewyZf9jd/K3sOpw2V2DntM92KaKS6E2pVyeXcDT2MNKIFGcply/Cl5agC5Wc+8xkAPv7xj886/ulPf7r7tRDiARMoLXPh+60z7nyeZeDZJomy8RwTJ5VMJfpi6C86RAlYhlYbv3lXjZofoZTq6EJqvUjTEARJSpjo5fbhnBqecdt1XPGDTy4LL+3DYQvtUyM6bpLYJkqq7n5fkCh68oJGGNMMYhIpMAyB7JRZ1cKYRGo/IM8ySZViuJpDdGp0Y6kYa0bU/JjV1TxCCJpBRBil5Ms5RhohA0WHZpgQJCmebVL2LGrtmFVlb0FZ6gMdDcuewxmePUut/tRVJXoKLr/bVV+QoVyWFV9ajipQ3nvvvYs9jhWNN4fHzSGRirTTSmKYAlPqJXPOtshZiiDtzDANoVsWTYNGEKOk3quUUtGIY8I0xdS6aIfdkzyUl/bbnvYGfrxEXtqHQ/ebKwSKVAncmbpD1yKWAkMofXOIJTnbxDK0urtjW0gptSWs1HWNMYK+osPaHo+9de2zXXH1/mAitXumUjDdTnAcE88xtb92zqQRpMwko2d80k0B7VjOm6U+lKOhABphqvcfV5VIOopNCzWUy1g6sgbURaAvv/B9vUiCZ+sAF6eKgmPSDBLiROFYglhqf/BUaaOwRjsmTnX5UMmzaPoJSUf8IYzlrFL3A2eUy9VL+3DMlDQ5lsA1jc4y1GBNNYdtGtw+0iCIUlzLwDbNrn5mybEJpaQR6MCiMLBMraq0tx5Rck1cy6QZJTSDhJ68Q5xKxsKQQs7ENl1SpWeuYaxrJGe6GaM0xe6oNR1oM3woFuJoWPPj7hI97xx8Kc5la5yxNGSBchEoHoHggdHxWQniCNuA0LUIE0Xe0XuS5ZxN2dPufWkqGWuFOKYOh/V2glQK14JGqLttzK6vjf66nawwL21mB/icLSjmLCo5m5ofESQpdH5nFc/Bsw0afgLoLhzPNvFsk4JrMtWKcS0TU0jyrk2vZ2l/m1TXWw6UdJuhaxr05G3GmxGbBgqs68mza9pnpBZQdEy2T/ms79UeNkppAeWBkkM7StjQV1iwwMV8joYHLtH3X34fytY4Y2nI/gKLQK0dLfixqYKOrQyphCRNME1BGEMkJa6ppb+aUUqiFFIqTNvsPjaIta+N7BSSz6h2RzqezOml/eOND+NtT3sDo6W+xXvTi4DZUR2f8atRgGebSKlLbNJO4eRA2aHg2ARJSs62KeckfixxbV2E71gGltBB1hQ2xZxBIaeTKH6k8KOYup9gmRHrez3OWFvhjKEyv9tdJ+kEsDXlHBPNkFqQ6KAF1MMIP5LYpsAQBmXPPmKh3cM5Gh5qiZ4ZjC0/skC5COxtxgt+7ExtetdHWugi8xSFihVKQjvWBlaiowsZphJDQZRKonTuInJnhXppG3T6tg3dtZSztaqPIQQF16Scs3nwmio1P6EeREgpyTsGYaqoBzGeZQAWkRAEsUIphWlaDBZcHNOkv9dGSY96EDPZjjhlqMTjNg/QV9QCvTNL4yiRrK3m6S/munWUtVaM51pdTcljUde4kCV6xtKTBcpFQCULD5T7W+ooOn3YUvfPG0LPrGxDEHcy22GqA+XhWKle2lLpInwldEKq4mk5s6RT/lPO6YxxLdAqPVPtiIl2hB8lOKaBECaJAtM0yNmCWlviuSZl16RasCnmLMI4JU71/u9AyeXsdT3d4DPX0lh35qRESUrQKf6f6cw5VjO7+ZboGUtPFigXgfEjmFEemJmeyVgnnVjopxIbXXM5H4fy0v7I4/6Mzz7iOcvWJnZ/UsBSICTUgoR6IBFCgmmwqxYz0ojZMd3GxCBVUhfTS8i5Jn1Fh8lWRN2PaIcCzzE5ZbDEaUNlWrH20F6/Ok8qFXGqkFIyWM7NOv9cS+OKZ6CbJo8fh1uiZyw9WaBcBBrBwvcoF8JCguTJEzv5yPeu5CF77ugeu3XwZC555iXcMXDSoo7nWJOiVdVVkJJzFLZh4IcxYaptKYJIu0OapkHOEvTkHRzboBmmbOgrkCjFUCnHmmqOpFNgbpuCqXbUTb7snPazxEjGUZN9ahaBejOc/0GLxEry0p4P2+hY/HZm07HS3ySm3oud2YpQgFSdfdrEYKBsc3Kfxx0jLXqKDif1FVBSsWmwxPbJNmPNkGLOJEokjc7eZJYYybg/ZIFyEZhc3AnlIVlJXtrzIdCdR6CX3SZ6ZpkqcIWBbUhSpYvxhSmwLbOjwCOQqcIwdIF4EKU4pkkrSfAcs9suOFoLaIYJYZJyUv+xScRkPHDIAuUi4C98i/LoUIrn3vpD3v0/n6Ic7RPg+PzDnsUHH/9SAjt3mCcvTxR05dFgn9WvQne/ONoTTZdApQpTKCxHUPVcwkQy3dbeN3Vfe5zbpsAyDQqOxek5C8sQDFVynHtSL4YQxFJR8+MsSZJxVGSBchE4lnHyUF7ab336m/j5EnppLxamAXO5h8iOCK9C+3TblvY4b4UxzSDBjxNcy0Sg+NU2OHdjD55ldNXFB8s5NvUXuWOkmcmXZdxvskC5jHnSXf/H+6/+x2Xnpb0YzHTjJOnBlQAzgsMz8TOSIKKEyDA6/e9Qci0c2yRJtZzarumAu0Yb9BZc1vdq+bK7xpqZfFnGopAFymXIXF7a4/kKf/2U1y4LL+3FYCZQzhUkZ+pLZ1ASYgFGKlFAwXWwLO3bvaFH2yxYlsHqisejTu6jlLO44b6pTL4sY9HIAuUy41Be2u98yuuYKFSXbmCLhNjv/4ahg6Dc75iAgzzSU8BW2sohZxmsqeQ4eaDEYEkXaoeppN7W2W2EtogYrQeZfFnGopEFymVC7hBe2pdf+Cq+/aALlmUL4pFiAK4FhiEwhMAyBFGSkqRaMUnOOEhKHURnzMRc28AUkLctTh0q4bkWG/ryXcUdxzRRxF1rWCCTL8tYVLJAuQw4e/cdfPR7V7Jpcmf32HL00j5aDLRxWtGxWFV2GGsmeLbBaUMltk/61PwYgSJMJe0wJVYKzxKITkDd2F8glYrJVqz9zFG0I4lSCaYhUCgEuld8RpIsky/LWEyyQLmE2GnM637+DV57gJf2+59wEV966DOWjZf2keAa+9oxFdqD2za0mnvRtXBti3W9NkPlHA9eV2Vtb8DtuxtMtQMm2gkFR2lRY/SSe6DocPKAVtipBQljjZAklUiE1owUgkRJevMuG/eTQMvkyzIWk+yTskRsGdvGld/76Cwv7ZtWb+Ety8xL+0jwTMjZFn6cdMp6BD0Fl5xtMlTJsabqUerUOJZzOgCWczbNIEGi8BNFGM8kcxQDBZfNgyU8xyJJoeD4jDQihBCsqQhMSzDdimlFKT15m/6i2w2KmXxZxmKSBcrjzD4v7S/jproCMzZM/uG8F/FPj3rBsvLSPhIEUMiZKMBMtRp4wbXxOlqahhAEsaQVhWzozbOmmuNRG3tBCM7d2Mt9402uu3OM3+ysUXIMlBAUXZuCYyKlwo8TLZtW1M6G9TCBEMo5m7PWVSjmHMZbEZuU0gmbTL4sYxHJAuVxZC4v7Tv613PJM5afl/aRIICcKXBti6pnU3RN6n5CmEgc2yBvW1TzNkGckqSSvfWAs9ZVKHt2VzVn82CRTYMlvF9uZ28tYG1PjrqfMtWOaIUJpgGFnM1j1lU4Z7hKM0y1U2JOS7O1o/SgTHYmX5axWGSB8nigFH/y2+9z6Qry0jbRvdiWKUikQkrdhy3QGWokIHQW27MthKFnd2euqXDKqhI7p9rcvKtGK0zxHG0aVvFsHGumCOjgsp2TB4q84GHDfO93e9hbD/Asg6GKS84ugFI0goQHrdGzx+IBXZuHymRn8mUZi0EWKI8xA81JPvjfH+OCe37VPbYcvbRnMAHb1A6FBcci55i4pkE9TBhvhJgGGMLAMgXVvM0pq0q6QyaRWIbBOeur9OVdRhohp6wq0Y7SThLHpOCY9ORd+op6djlXHePmVSVenLf57a4ae6YDpFJUchbFnM3eWoBjz701kWWyM44lSxYo4zjmzW9+M1/72tcAePGLX8yVV16JZZ04sXtuL+2n8t7z/2JZeWnvj2Xo/mvTNMg5FhsHi5x3ch87p1pcc9sYBcekmnfoLzoMlnMYhqDgWPQVHEbqAYYQ7K4FtMKETYNFhqselmmSSIVlCPKONuzaU/MPWcfYX8pxwanuQcrjN9w3lWWyM5aEJftUXXHFFfz0pz/l1lv1ft3TnvY03ve+93HZZZct1ZAWjZXmpT2DiRaiEMJgsOjw4HW9nLuxl7U9eYZ7PaJEEaWKsmtiGAaOZVAtOKyt5KiHKedt7ue01WWm2hE5x2Cw5JJ3Dl72+nEy7+xvriVzlsnOWCqWLFB+7nOf48orr2T1au3p8td//de89a1vnTNQhmFIGO4Tx63X68dtnEfKcvTSPtDv+8Cf2Qb0F3VtYy1Mefj6Hh69uY/BUg4hDJRSTLYTHnfKAEmqGGuGlHM2BdfCNGCiFetAtapENe9Q8WxG6mFn9mct2uwvy2RnLBVLEiinpqbYuXMn55xzTvfYOeecw/bt26nValQqlVmPf//738/ll19+nEd5ZCw3L23PBs+ySKQkShVJqnRPdSdiGp3YZVuCoVKOTYMlWlHCmpzDhoEipZyDUgI/Troztodt6AXoBqq6H88ZqI6lDWuWyc5YCoRS6lCTjWPGjh07WL9+PWNjY/T39wMwNjbG4OAgO3bsYN26dbMeP9eMcnh4mFqtRrm8NLO0/Xn+Sz50zLy0HaGzzErp/cNEdpwbOz/fP4dsGuA6BkJCqWBjYlDMmTTDBNs0COOUaT8iiBWmgJxjMFD0WNvjUcnZ1IOYx586yKlDJcYa4SE1HJVSCwpUE81wlh1spgeZsdyo1+tUKpV5Y8mSzCiLRa2lWKvVuoGyVtOai6VS6aDHu66L6y7DCysI4LLL+OZXP7zoXtqeCf1lF882qeRdCrZJmKS0woRUKSZbIX4kSaSk5NpYptDdMJZJT15btQoEcSpZ1+ORs0zum2iDECilWFPJccqqMoNllyBRTPsRfUWHx586yEl9+cMGwoWW3GSzv4wThSUJlD09Paxbt46bbrqJTZs2AXDTTTcxPDx80LJ72XLTTfBnfwa33MJMSmIxvLQHizZbVmmPl/5Sjt21gJN68/iR5O6xJkMVcC2TkYauxyzYJmeuq1DulM+EqRaCmGiGNMMU1zLI2yZSKab8hGrB4VEbe3Esk6l2RDuSmKaglLN52PoeTurLL2rtYVbHmHEisGTJnIsuuoj3vve9nHfeeQC8733v4+Uvf/lSDWfhJAl88IPw7nfrr4HItPjw/fDSFkDRFWzoK3DBqYOYpkkpZzNQcvGjlGaUEMSKTQMFNvQV8BwLP4rZPe0zWg8pOjalnM2mwRKbBwrYlsloPWD7VJtGO6IRpBgGnLO+hzBOEYZBb95muCdPK0qoBwkDJYeHbejJZnsZGXOwZIHy0ksvZWJigtNPPx3QdZR/9Vd/tVTDWRh33gl//ufwi1/sO3bOOVz69NfzjXTVvE93gE1DRTzbIJUKP5JEaYpnm5yzocofnbOOU1dXiJOUu8dajNR8UgWTjYhN/UWGe/OUO7Ozcs4iShWnralw1toKjmXOWtbOtAUeuOydbEUH7RueOlTK9g0zMg7DkiRz7i8L3YBdNKSET34S3vY28H19zDDgne+Eyy7jrd+5mX+9aWTel1nfY3P6UA+lvE2SpIw1Y/KOwUl9Bf70kevZOLBvf3YmYTJaD7h51zRJqugvugdlj4/G+2WhyZiMjBOdZZ3MWVHs2AEXXQTX7vOvYcsW+NKX4FGPAuD0NWVYQKAsuTaeY7G6kqMRJAyUc1TzLg9bX+Wk/tlmYTN7exXPprfgLGrtYLZvmJFxZGSB8lAoBV/+MrzhDVDb54LI616n9yjz+1oQT19TwTYgnsdZIE4hTFLSVDJYcsnZFqurObasKh12RpdljzMylpYsUM7F2Bi86lXw3e/uO7ZuHXz+83DhhQc9fNNAiU0DRe4aaR5kjAU6WdNfcHj0pj7W9BTwHJNKzmJVxVvwrDCbBWZkLB1ZoDyQf/93eMUrdLCc4c//HP7hH6BanfMpOcfmKWeuohVFjNYiIrmvZdAEKnmLx5/Sx0vPO5m+opvNCjMyVhhZoJyhVoM3vhG++MV9x/r74dOfhj/6o8M+tZyzOHNthdv2NsjbDabaCYlUGEJQ8Qw29JV40plDbOwvZIExI2MFkgVKgB/+EF72Mp24meEP/xA+9SlYNX/Zj0awqpxjfY9HIhWJ7AjfGoIwVYgVaBSWkZGheWAHynZbl/h87GP7jpXLepn90pcuuAWxHiQEccrDNvQw0YyZaockqcIyxbxCtRkZGcufB26gvOEGvfd4xx37jl1wgU7YrF9/RC8Vp5IokayueAyWcrSj/BEJ1WZkZCxvHnjrwSiCyy6DxzxmX5DM5fQs8n/+54iDJIBtahHbIEkRQlBwLSqe1msUQmQ2BRkZK5wH1ozy1lu1kMVvfrPv2CMeoYvHTzvtqF+2nLMYLOcym4KMjBOUB8YUJ03hIx+Bhz1sX5C0LHjPe+DnP79fQRL2CdWWchY7p33aUYKUinaUsHPaz2wKMjJWOCf+FMf34SlPgeuu23fsjDN0181DH7pop8lsCjIyTlxO/EDpeXDyyTpQCgGXXAJXXKH3JReZrNUwI+PE5MQPlKATNffdp5faf/AHx/RUWathRsaJxwMjUFYq8OMfL/UoMjIyVigPjGRORkZGxv0gC5QZGRkZ85AFyoyMjIx5yAJlRkZGxjysyGTOjM1PvV5f4pFkZGSsZGZiyHzWYSsyUDYaDQCGh4eXeCQZGRknAo1Gg0qlcsifr0gXRiklu3fvplQ6vNfMUlGv1xkeHmbHjh3HxyXyOHKivrcT9X3BifveFuN9KaVoNBqsWbMGwzj0TuSKnFEahsG6deuWehjzUi6XT6gP5v6cqO/tRH1fcOK+t/v7vg43k5whS+ZkZGRkzEMWKDMyMjLmIQuUxwDXdfmbv/kbXPfEUww6Ud/bifq+4MR9b8fzfa3IZE5GRkbG8SSbUWZkZGTMQxYoMzIyMuYhC5QZGRkZ85AFyoyMjIx5yALlIhPHMa973evo7e2lt7eX17/+9SRJstTDut+EYcgrXvEKNm7cSKlU4rTTTuNzn/vcUg9rUfF9n82bN1OtVpd6KIvKVVddxTnnnEOhUGDNmjX88z//81IP6X6za9cunvOc59DX10d/fz8veMELGBkZOWbnywLlInPFFVfw05/+lFtvvZVbb72V6667jve9731LPaz7TZIkrF69mmuuuYZ6vc4XvvAF3vKWt/CDH/xgqYe2aFx22WUrouPrSLj66qu5+OKL+fu//3vq9Tq33norT3jCE5Z6WPebiy++GIBt27Zx7733EoYhb3zjG4/dCVXGorJu3Tr1rW99q/v9N7/5TbV+/folHNGx44/+6I/UpZdeutTDWBRuvPFGdcYZZ6irr75aVSqVpR7OovHwhz9cfepTn1rqYSw6D37wg9VXv/rV7vdf+cpX1JlnnnnMzpfNKBeRqakpdu7cyTnnnNM9ds4557B9+3ZqtdrSDewYEAQBN9xwA2edddZSD+V+kyQJr3jFK/jEJz5xQhVlt1otbrzxRur1OqeddhpDQ0O88IUvZO/evUs9tPvNJZdcwre+9S1qtRrT09N8/etf5xnPeMYxO18WKBeRZrMJMGuPa+brGWm4EwGlFC9/+cvZsmULz33uc5d6OPebj3zkI5x11lknxJJ0f6amplBK8eUvf5nvf//73H333di2zZ/92Z8t9dDuN+eddx6jo6P09PTQ29vL5OQk73rXu47Z+bJAuYgUi0WAWbPHma9LpdKSjGmxUUrxmte8hjvuuIN/+7d/O6w01Upg69atfOITn+DDH/7wUg9l0Zn5PL7hDW9gw4YNFItFLr/8cq699lpardYSj+7okVLypCc9ifPOO49ms0mz2eSxj30sT3nKU47ZOVf2p3yZ0dPTw7p167jpppu6x2666SaGh4cXJOW03FFK8drXvpYbbriBH/zgByfEe7ruuusYGxvjzDPPZGhoiOc+97nU63WGhoa44YYblnp494tqtcr69evn1GxVK7hzeXJykm3btvGGN7yBfD5PPp/n9a9/Pddffz3j4+PH5qTHbPfzAcqll16qHvKQh6g9e/aoPXv2qIc85CHq8ssvX+phLQoXX3yxOuuss9T4+PhSD2XRaLfb3b/Vnj171Le//W1VLpfVnj17VBRFSz28+80VV1yhzj77bLVz507VbrfVn//5n6sLL7xwqYd1v9m8ebN6xzveoXzfV77vq7e//e1q3bp1x+x8WaBcZKIoUhdffLGqVquqWq2q1772tSqO46Ue1v3mvvvuU4ByXVcVCoXuv1e96lVLPbRF5Uc/+tEJlfVOkkRdcsklqq+vT/X19annP//5as+ePUs9rPvNrbfeqp785Cer3t5eVa1W1fnnn69+/etfH7PzZepBGRkZGfOQ7VFmZGRkzEMWKDMyMjLmIQuUGRkZGfOQBcqMjIyMecgCZUZGRsY8ZIEyIyMjYx6yQJmRkZExD1mgzMjIyJiHLFBmZGRkzEMWKDOWJSeddBL/9m//tmTnf9nLXsab3vSmJTt/xvIiC5QZJxwngkdRxvIiC5QZy44XvOAFbN++nRe96EUUi0Ve/epX87a3vY0NGzZQKpU444wz+Na3vtV9/I9//GOq1Sr/9E//xPr163n0ox8NwD/+4z8yPDxMX18f73rXuzjnnHP4whe+0H3eNddcw7nnnku1WuXMM8/kqquuAuBjH/sYX/3qV/nkJz9JsVjkzDPPPK7vP2MZcszkNjIy7gcbNmxQ3/3ud7vff+UrX1EjIyMqSRL19a9/Xbmuq+655x6llFb8MQxDvepVr1KtVku1Wi11zTXXqGq1qn7xi1+oMAzVpZdeqizLUp///OeVUkr99re/VdVqVV177bUqTVN13XXXqXK5rG6//XallFIvfelL1Rvf+Mbj/K4zlivZjDJjRfDiF7+YwcFBTNPkT/7kTzjttNP4+c9/3v25lJIPfOADXSHXr33ta7z4xS/m3HPPxXEcLr30UgqFQvfxn/rUp3jZy17GBRdcgGEYPPaxj+WZz3wm3/zmN5fi7WUsc6ylHkBGxkK48sor+exnP8vOnTsRQtBsNmepWZdKpVleRbt3757lgWPbNqtXr+5+f9999/HDH/6Qz3/+891jSZJQLpeP6fvIWJlkgTJjWbK/F89Pf/pT3v3ud/PDH/6QhzzkIRiGwTnnnDPLzuBA7541a9awY8eO7vdJkrBnz57u98PDw7zxjW/kAx/4wLznz8jIPg0Zy5JVq1axdetWAOr1OpZlMTAwgJSSz33uc9xyyy2Hff6LXvQivva1r/GrX/2KOI654oorZhlqvepVr+Lzn/88P/rRj0jTlDAMuf7667ntttu657/nnnuO3RvMWFFkgTJjWfJXf/VXfPzjH6enp4f//M//5HnPex4PfvCDWbNmDbfeeivnnXfeYZ9/4YUX8jd/8zc85znPYWhoiCRJOOWUU7q+3Q95yEP4+te/zrve9S4GBgZYu3Ytl156KWEYAvDyl7+cXbt20dPTc0J4l2fcPzIriIwHBFEU0dfXx3//93/z2Mc+dqmHk7HCyGaUGScs3/nOd/B9n1arxdvf/nZ6e3s599xzl3pYGSuQLFBmnLB8+ctfZvXq1axZs4Ybb7yRf//3f8dxnKUeVsYKJFt6Z2RkZMxDNqPMyMjImIcsUGZkZGTMQxYoMzIyMuYhC5QZGRkZ85AFyoyMjIx5yAJlRkZGxjxkgTIjIyNjHrJAmZGRkTEP/z9+4Ux+h9ZzVwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 354.331x218.989 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.44878078555627543"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plot the predicted vs target values parity plot\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.scatter(df['True_Label'], df['Prediction'], alpha=0.25)\n",
+    "plt.xlabel('target')\n",
+    "plt.ylabel('predicted')\n",
+    "\n",
+    "x = np.linspace(-1, 7, 6)\n",
+    "y = x\n",
+    "plt.plot(x, y, '-r', label='parity')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# pring the mean absolute error\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "\n",
+    "mean_absolute_error(df['True_Label'], df['Prediction'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cgcnn",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/packages.txt b/packages.txt
new file mode 100644
index 00000000..c33ad1db
--- /dev/null
+++ b/packages.txt
@@ -0,0 +1,259 @@
+Package                           Version
+--------------------------------- -----------
+absl-py                           1.4.0
+aflow-ml                          2.0
+anyio                             3.6.2
+appnope                           0.1.3
+argon2-cffi                       21.3.0
+argon2-cffi-bindings              21.2.0
+arrow                             1.2.3
+ase                               3.22.1
+asttokens                         2.2.1
+astunparse                        1.6.3
+attrs                             22.2.0
+autopep8                          2.0.2
+Babel                             2.12.1
+backcall                          0.2.0
+backports.functools-lru-cache     1.6.4
+beautifulsoup4                    4.12.2
+black                             23.3.0
+bleach                            6.0.0
+bokeh                             3.1.0
+boltons                           23.0.0
+bravado                           11.0.3
+bravado-core                      5.17.1
+brotlipy                          0.7.0
+cached-property                   1.5.2
+cachetools                        5.3.0
+certifi                           2023.7.22
+cffi                              1.15.1
+charset-normalizer                3.1.0
+cif2cell                          2.0.0a3
+click                             8.1.3
+colorama                          0.4.6
+comm                              0.1.3
+contourpy                         1.0.7
+cryptography                      40.0.2
+cssselect                         1.2.0
+cycler                            0.11.0
+Cython                            0.29.34
+debugpy                           1.6.7
+decorator                         5.1.1
+defusedxml                        0.7.1
+dgl                               1.1.2
+dnspython                         2.3.0
+emmet-core                        0.58.1
+entrypoints                       0.4
+executing                         1.2.0
+fastjsonschema                    2.16.3
+filetype                          1.2.0
+flatbuffers                       23.3.3
+flatten-dict                      0.4.2
+flit_core                         3.8.0
+fonttools                         4.39.3
+fqdn                              1.5.1
+frozendict                        2.3.8
+future                            0.18.3
+gast                              0.4.0
+ghp-import                        2.1.0
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