diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..fc0d998c --- /dev/null +++ b/.gitignore @@ -0,0 +1,12 @@ +__pycache__ +*.egg-info/ +*.eggs/ +*.data + +#neuralnetwork-generated +data + +old +deploy-notes +venv +secret.py diff --git a/README.md b/README.md index 414438d3..c5d3f305 100644 --- a/README.md +++ b/README.md @@ -1,66 +1,49 @@ -# ReWoTes +# mlbands -REal WOrld TEstS +A python package that implements automatic prediction of electronic band gaps for a set of materials based on training data. -## Overview -This repository contains example test assignments used during our hiring process. +## Installation -We find that regular job interview questions can often be misleading and so use more engaged "real-world" examples instead. +```ruby +pip install --upgrade mlbands +``` -Each file represents an assignment similar to what one would get when hired. +## Documentation and Usage on Google Colab (click below) -| Focus | ReWote | Keywords | -| ---------------| --------------------------| ------------------------------- | -| Comp. Science | [Convergence Tracker](Convergence-Tracker.md) | Python, OOD, DFT, Planewaves | -| Comp. Science | [Basis Set Selector](Basis-Set-Selector.md) | Python, OOD, DFT, Local-orbital | -| Data. Science | [ML Property Predict](ML-Band-Gaps.md) | Python, ML Models, Scikit, Featurization | -| Front-End / UX | [Materials Designer](Materials-Designer.md) | ReactJS / UX Design, ThreeJS | -| Front-End / UX | [Flowchart Designer](Flowchart-Designer.md) | ReactJS / UX Design, DAG | -| Back-End / Ops | [Parallel Uploader](Parallel-File-Uploader.md) | Python, OOD, Threading, Objectstore | -| CI/CD, DevOps | [End-to-End Tests](End-To-End-Tests.md) | BDD tests, CI/CD workflows, Cypress | -| HPC, Cloud Inf | [Cloud HPC Bench.](Cloud-Infrastructure.md) | HPC Cluster, Linpack, Benchmarks | -| HPC, Containers| [Containerized HPC](Containerization-HPC.md) | HPC Cluster, Containers, Benchmarks | + + -## Usage -We suggest the following flow: -1. [Fork](https://docs.github.com/en/free-pro-team@latest/github/getting-started-with-github/fork-a-repo) this repository on GitHub -2. Create a branch using your GitHub username as a branch name -3. Create a subfolder with your GitHub username -4. Copy one of the ReWoTe suggestions (`.md` files) to `README.md` in that subfolder and modify the content of the ReWoTe as necessary -5. Introduce any changes under the subfolder -6. Submit a [pull request](https://docs.github.com/en/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork) into the `dev` branch of this repository +# ML Band Gaps (Materials) -See [dev branch](https://github.com/Exabyte-io/rewotes/tree/dev) also. +> Ideal candidate: skilled ML data scientist with solid knowledge of materials science. -## Notes +# Overview -Examples listed here are only meant as guidelines and do not necessarily reflect on the type of work to be performed at the company. Modifications to the individual assignments with an advance notice are encouraged. +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. -We will screen for the ability to (1) pick up new concepts quickly, (2) implement a working proof-of-concept solution, and (3) outline how the PoC can become more mature. We value attention to details and modularity. +# 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. -## Hiring process +# Requirements -Our hiring process in more details: +- 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) -| Stage | Target Duration | Topic | -| ----------------- | ----------------- | ------------------------------ | -| 0. Email screen | | why mat3ra.com / exabyte.io | -| 1. Phone screen | 15-20 min | career goals, basic skillset | -| 2. ReWoTe | 1-2h x 2-5 days | real-world work/thought process| -| 3. On-site meet | 3-4 x 30 min | personality fit | -| 4. Discuss offer | 30 min | cash/equity/benefits | -| 5. References | 2 x 15 min | sanity check | -| 6. Decision | | when to start | +# Expectations -TOTAL: ~2 weeks tentative. +- 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 -## Contact info +We leave exact timing to the candidate. Must fit Within 5 days total. -With any questions about this repository or our hiring process please contact us at info@mat3ra.com. +# Notes -© 2022 Exabyte Inc. +- use a designated github repository for version control +- suggested source of training data: materialsproject.org diff --git a/Basis-Set-Selector.md b/all-rewotes/Basis-Set-Selector.md similarity index 100% rename from Basis-Set-Selector.md rename to all-rewotes/Basis-Set-Selector.md diff --git a/Cloud-Infrastructure.md b/all-rewotes/Cloud-Infrastructure.md similarity index 100% rename from Cloud-Infrastructure.md rename to all-rewotes/Cloud-Infrastructure.md diff --git a/Containerization-HPC.md b/all-rewotes/Containerization-HPC.md similarity index 100% rename from Containerization-HPC.md rename to all-rewotes/Containerization-HPC.md diff --git a/Convergence-Tracker.md b/all-rewotes/Convergence-Tracker.md similarity index 100% rename from Convergence-Tracker.md rename to all-rewotes/Convergence-Tracker.md diff --git a/End-to-End-Tests.md b/all-rewotes/End-to-End-Tests.md similarity index 100% rename from End-to-End-Tests.md rename to all-rewotes/End-to-End-Tests.md diff --git a/Flowchart-Designer.md b/all-rewotes/Flowchart-Designer.md similarity index 100% rename from Flowchart-Designer.md rename to all-rewotes/Flowchart-Designer.md diff --git a/ML-Band-Gaps.md b/all-rewotes/ML-Band-Gaps.md similarity index 100% rename from ML-Band-Gaps.md rename to all-rewotes/ML-Band-Gaps.md diff --git a/Materials-Designer.md b/all-rewotes/Materials-Designer.md similarity index 100% rename from Materials-Designer.md rename to all-rewotes/Materials-Designer.md diff --git a/Parallel-File-Uploader.md b/all-rewotes/Parallel-File-Uploader.md similarity index 100% rename from Parallel-File-Uploader.md rename to all-rewotes/Parallel-File-Uploader.md diff --git a/dist/mlbands-1.0.0-py3-none-any.whl b/dist/mlbands-1.0.0-py3-none-any.whl new file mode 100644 index 00000000..54f0e188 Binary files /dev/null and b/dist/mlbands-1.0.0-py3-none-any.whl differ diff --git a/dist/mlbands-1.0.0.tar.gz b/dist/mlbands-1.0.0.tar.gz new file mode 100644 index 00000000..99dc3765 Binary files /dev/null and b/dist/mlbands-1.0.0.tar.gz differ diff --git a/dist/mlbands-1.0.1-py3-none-any.whl b/dist/mlbands-1.0.1-py3-none-any.whl new file mode 100644 index 00000000..693d4cf4 Binary files /dev/null and b/dist/mlbands-1.0.1-py3-none-any.whl differ diff --git a/dist/mlbands-1.0.1.tar.gz b/dist/mlbands-1.0.1.tar.gz new file mode 100644 index 00000000..7944ac01 Binary files /dev/null and b/dist/mlbands-1.0.1.tar.gz differ diff --git a/draft/GdSnPd.cif b/draft/GdSnPd.cif new file mode 100644 index 00000000..6d7ad1a7 --- /dev/null +++ b/draft/GdSnPd.cif @@ -0,0 +1,38 @@ +# generated using pymatgen +data_GdSnPd +_symmetry_space_group_name_H-M 'P 1' +_cell_length_a 4.65008947 +_cell_length_b 7.31311105 +_cell_length_c 7.95465153 +_cell_angle_alpha 90.00000000 +_cell_angle_beta 90.00000000 +_cell_angle_gamma 90.00000000 +_symmetry_Int_Tables_number 1 +_chemical_formula_structural GdSnPd +_chemical_formula_sum 'Gd4 Sn4 Pd4' +_cell_volume 270.51081729 +_cell_formula_units_Z 4 +loop_ + _symmetry_equiv_pos_site_id + _symmetry_equiv_pos_as_xyz + 1 'x, y, z' +loop_ + _atom_site_type_symbol + _atom_site_label + _atom_site_symmetry_multiplicity + _atom_site_fract_x + _atom_site_fract_y + _atom_site_fract_z + _atom_site_occupancy + Gd Gd0 1 0.25000000 0.51062534 0.20448969 1 + Gd Gd1 1 0.25000000 0.01062534 0.29551031 1 + Gd Gd2 1 0.75000000 0.48937466 0.79551031 1 + Gd Gd3 1 0.75000000 0.98937466 0.70448969 1 + Sn Sn4 1 0.25000000 0.69150951 0.58737792 1 + Sn Sn5 1 0.25000000 0.19150951 0.91262208 1 + Sn Sn6 1 0.75000000 0.30849049 0.41262208 1 + Sn Sn7 1 0.75000000 0.80849049 0.08737792 1 + Pd Pd8 1 0.25000000 0.79706757 0.91570644 1 + Pd Pd9 1 0.25000000 0.29706757 0.58429356 1 + Pd Pd10 1 0.75000000 0.20293243 0.08429356 1 + Pd Pd11 1 0.75000000 0.70293243 0.41570644 1 diff --git a/draft/GdSnPd.json b/draft/GdSnPd.json new file mode 100644 index 00000000..743ec76f --- /dev/null +++ b/draft/GdSnPd.json @@ -0,0 +1,129 @@ +{ + "@module": "pymatgen.core.structure", + "@class": "Structure", + "charge": 0, + "lattice": { + "matrix": [ + [4.65008947, 0.0, 2.847358592592156e-16], + [1.1760379519337024e-15, 7.31311105014398, 4.477989019683982e-16], + [0.0, 0.0, 7.954651530160617] + ], + "pbc": [true, true, true], + "a": 4.65008947, + "b": 7.31311105014398, + "c": 7.954651530160617, + "alpha": 90.0, + "beta": 90.0, + "gamma": 90.0, + "volume": 270.51081728514777 + }, + "sites": [{ + "species": [{ + "element": "Gd", + "occu": 1 + }], + "abc": [0.25, 0.51062534, 0.2044896850000001], + "xyz": [1.1625223675000007, 3.734259816437527, 1.6266441856873135], + "label": "Gd", + "properties": {} + }, { + "species": [{ + "element": "Gd", + "occu": 1 + }], + "abc": [0.25, 0.010625340000000039, 0.2955103149999999], + "xyz": [1.1625223675, 0.07770429136553712, 2.350681579392995], + "label": "Gd", + "properties": {} + }, { + "species": [{ + "element": "Gd", + "occu": 1 + }], + "abc": [0.75, 0.48937465999999996, 0.7955103149999999], + "xyz": [3.4875671025000003, 3.5788512337064526, 6.328007344473304], + "label": "Gd", + "properties": {} + }, { + "species": [{ + "element": "Gd", + "occu": 1 + }], + "abc": [0.75, 0.98937466, 0.7044896850000001], + "xyz": [3.4875671025000012, 7.235406758778443, 5.603969950767622], + "label": "Gd", + "properties": {} + }, { + "species": [{ + "element": "Sn", + "occu": 1 + }], + "abc": [0.25, 0.69150951, 0.5873779150000001], + "xyz": [1.162522367500001, 5.057085838860649, 4.672386630337304], + "label": "Sn", + "properties": {} + }, { + "species": [{ + "element": "Sn", + "occu": 1 + }], + "abc": [0.25, 0.19150950999999994, 0.9126220849999999], + "xyz": [1.1625223675000003, 1.4005303137886584, 7.259590664903621], + "label": "Sn", + "properties": {} + }, { + "species": [{ + "element": "Sn", + "occu": 1 + }], + "abc": [0.75, 0.30849048999999995, 0.41262208499999986], + "xyz": [3.4875671025000003, 2.2560252112833306, 3.2822648998233133], + "label": "Sn", + "properties": {} + }, { + "species": [{ + "element": "Sn", + "occu": 1 + }], + "abc": [0.75, 0.80849049, 0.08737791500000025], + "xyz": [3.487567102500001, 5.91258073635532, 0.6950608652569968], + "label": "Sn", + "properties": {} + }, { + "species": [{ + "element": "Pd", + "occu": 1 + }], + "abc": [0.25, 0.79706757, 0.915706435], + "xyz": [1.162522367500001, 5.82904365387841, 7.284125594350674], + "label": "Pd", + "properties": {} + }, { + "species": [{ + "element": "Pd", + "occu": 1 + }], + "abc": [0.25, 0.29706756999999984, 0.584293565], + "xyz": [1.1625223675000005, 2.172488128806419, 4.647851700890252], + "label": "Pd", + "properties": {} + }, { + "species": [{ + "element": "Pd", + "occu": 1 + }], + "abc": [0.75, 0.20293243000000005, 0.08429356499999996], + "xyz": [3.4875671025000003, 1.4840673962655702, 0.6705259358099435], + "label": "Pd", + "properties": {} + }, { + "species": [{ + "element": "Pd", + "occu": 1 + }], + "abc": [0.75, 0.70293243, 0.41570643500000015], + "xyz": [3.487567102500001, 5.14062292133756, 3.306799829270367], + "label": "Pd", + "properties": {} + }] +} diff --git a/draft/ML_LeNet5-example.py b/draft/ML_LeNet5-example.py new file mode 100644 index 00000000..1ea7aee0 --- /dev/null +++ b/draft/ML_LeNet5-example.py @@ -0,0 +1,136 @@ +# Load in relevant libraries, and alias where appropriate +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms + +from mlbands.neuralnet import LeNet5 + + +def ML_run(): + # Define relevant variables for the ML task + batch_size = 64 + num_classes = 10 + learning_rate = 0.001 + num_epochs = 10 + + # Device will determine whether to run the training on GPU or CPU. + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + + + #Loading the dataset and preprocessing + train_dataset = torchvision.datasets.MNIST(root = './data', + train = True, + transform = transforms.Compose([ + transforms.Resize((32,32)), + transforms.ToTensor(), + transforms.Normalize(mean = (0.1307,), std = (0.3081,))]), + download = True) + + + test_dataset = torchvision.datasets.MNIST(root = './data', + train = False, + transform = transforms.Compose([ + transforms.Resize((32,32)), + transforms.ToTensor(), + transforms.Normalize(mean = (0.1325,), std = (0.3105,))]), + download=True) + + + train_loader = torch.utils.data.DataLoader(dataset = train_dataset, + batch_size = batch_size, + shuffle = True) + + + test_loader = torch.utils.data.DataLoader(dataset = test_dataset, + batch_size = batch_size, + shuffle = True) + + + # #Defining the convolutional neural network + # class LeNet5(nn.Module): + # def __init__(self, num_classes): + # super(LeNet5, self).__init__() + # self.layer1 = nn.Sequential( + # nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0), + # nn.BatchNorm2d(6), + # nn.ReLU(), + # nn.MaxPool2d(kernel_size = 2, stride = 2)) + # self.layer2 = nn.Sequential( + # nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0), + # nn.BatchNorm2d(16), + # nn.ReLU(), + # nn.MaxPool2d(kernel_size = 2, stride = 2)) + # self.fc = nn.Linear(400, 120) + # self.relu = nn.ReLU() + # self.fc1 = nn.Linear(120, 84) + # self.relu1 = nn.ReLU() + # self.fc2 = nn.Linear(84, num_classes) + + # def forward(self, x): + # out = self.layer1(x) + # out = self.layer2(out) + # out = out.reshape(out.size(0), -1) + # out = self.fc(out) + # out = self.relu(out) + # out = self.fc1(out) + # out = self.relu1(out) + # out = self.fc2(out) + # return out + + + model = LeNet5(num_classes).to(device) + + #Setting the loss function + cost = nn.CrossEntropyLoss() + + #Setting the optimizer with the model parameters and learning rate + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + + #this is defined to print how many steps are remaining when training + total_step = len(train_loader) + + + # Training the model + + total_step = len(train_loader) + for epoch in range(num_epochs): + for i, (images, labels) in enumerate(train_loader): + images = images.to(device) + labels = labels.to(device) + + #Forward pass + outputs = model(images) + loss = cost(outputs, labels) + + # Backward and optimize + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if (i+1) % 400 == 0: + print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' + .format(epoch+1, num_epochs, i+1, total_step, loss.item())) + + + + + # Test the model + # In test phase, we don't need to compute gradients (for memory efficiency) + + with torch.no_grad(): + correct = 0 + total = 0 + for images, labels in test_loader: + images = images.to(device) + labels = labels.to(device) + outputs = model(images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + + print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total)) + + +# ML_run() \ No newline at end of file diff --git a/draft/README.md b/draft/README.md new file mode 100644 index 00000000..79e78ea3 --- /dev/null +++ b/draft/README.md @@ -0,0 +1,10 @@ +# in development + +Currently running tests with data from https://materialsproject.org/materials/mp-1103503 +GdSnPd +mp-1103503 + +## running package manager (pipenv) +https://medium.com/dsckiit/managing-dependencies-in-python-a580ded4f67 + +To start environment, run `pipenv shell` diff --git a/draft/diffraction-patterns.py b/draft/diffraction-patterns.py new file mode 100644 index 00000000..e0179f71 --- /dev/null +++ b/draft/diffraction-patterns.py @@ -0,0 +1,37 @@ +from mp_api.client import MPRester +from pymatgen.analysis.diffraction.xrd import XRDCalculator +from pymatgen.symmetry.analyzer import SpacegroupAnalyzer +import secret + +with MPRester(api_key=secret.API_KEY) as mpr: + # first retrieve the relevant structure + structure = mpr.get_structure_by_material_id('mp-1103503') + +# important to use the conventional structure to ensure +# that peaks are labelled with the conventional Miller indices +sga = SpacegroupAnalyzer(structure) +conventional_structure = sga.get_conventional_standard_structure() + +# this example shows how to obtain an XRD diffraction pattern +# these patterns are calculated on-the-fly from the structure +calculator = XRDCalculator(wavelength='CuKa') +pattern = calculator.get_pattern(conventional_structure) + +# print('\nstructure:\n{}\n\nsga:\n{}\n\nconventional structure:\n{}'.\ +# format(structure,sga,conventional_structure)) + +# print('\npattern:\n',pattern) + + +# print(conventional_structure) + +print(conventional_structure.lattice) +print(conventional_structure.sites) #https://pymatgen.org/pymatgen.core.sites.html?highlight=periodicsite#pymatgen.core.sites.PeriodicSite + +Nsites = len(conventional_structure.sites) +for i in range(Nsites): + print('\n\n') + # print(conventional_structure.sites[i]) + print(conventional_structure.sites[i].species) + print(conventional_structure.sites[i].coords) + print(conventional_structure.sites[i].frac_coords) diff --git a/draft/mp-1103503 b/draft/mp-1103503 new file mode 100644 index 00000000..523e6cf7 --- /dev/null +++ b/draft/mp-1103503 @@ -0,0 +1,239 @@ + + + + + + + + + + + + mp-1103503: GdSnPd (Orthorhombic, Pnma, 62) + + + + + +
+
+ Loading... +
+
+ + + + \ No newline at end of file diff --git a/draft/thermo.py b/draft/thermo.py new file mode 100644 index 00000000..1c2bbf96 --- /dev/null +++ b/draft/thermo.py @@ -0,0 +1,14 @@ +from mp_api.client import MPRester +import secret + +with MPRester(api_key=secret.API_KEY) as mpr: + + # for a single material + # thermo_doc = mpr.thermo.get_data_by_id('mp-1103503') # DOES NOT WORK (WHY?) + + # for many materials, it's much faster to use + # the `search` method, where additional material_ids can + # be added to this list + thermo_docs = mpr.thermo.search(material_ids=['mp-1103503']) + + print(thermo_docs) diff --git a/mlbands/ML.py b/mlbands/ML.py new file mode 100644 index 00000000..a1633443 --- /dev/null +++ b/mlbands/ML.py @@ -0,0 +1,147 @@ +# Load in relevant libraries, and alias where appropriate +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms + +from torch.utils.data import Dataset, DataLoader +class Data(Dataset): + def __init__(self,X_train,Y_train): + self.X=torch.from_numpy(X_train).float() + self.Y=torch.from_numpy(Y_train).float() + self.len=self.X.shape[0] + def __getitem__(self,index): + return self.X[index], self.Y[index] + def __len__(self): + return self.len + +from mlbands.neuralnets import LeNet3D, LeNet5 + + +def reshapeX(array,channels=1): + return array.reshape((array.shape[0],1,*array.shape[1:])) + +def reshapeY(array): + if len(array.shape)==1: + return array.reshape(-1,1) + else: + return array + +def reshapeXY(data,channels=1): + + X,Y = data + # return [reshapeX(X,channels=1),Y] + return [reshapeX(X,channels=1),reshapeY(Y)] + + +# Device will determine whether to run the training on GPU or CPU. +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +class Machine: + def __init__(self, batch_size=1, num_classes=1, learning_rate=0.001, num_epochs=10 ): + '''Machine [ learning ] class for bulk calculations + + Parameters + ---------- + batch_size : int + batch size + num_classes : int + number of output ("Y") classes + learning_rate : float + learning rate for optimizer + num_epochs : int + number of training epochs + neuralnet: < neuralnets.neural_network Class> + neural network model (default: LeNet3D) + cost : + cost / loss function for training default: MSELoss + input_channels : int + number of input channels to neural network for (3-D structure, band_gap) (X,Y) combination, it is =1 + ''' + self.batch_size = batch_size + self.num_classes = num_classes + self.learning_rate = learning_rate + self.num_epochs = num_epochs + + self.neuralnet = LeNet3D + self.cost = nn.MSELoss() # the loss function + # self.cost = nn.CrossEntropyLoss() + self.input_channels = 1 + + + def learn(self, trainset, testset): + '''neural network training and testing (validation) + + Parameters + ---------- + trainset : < Group.X, Group.y > + training set of x and y values + testset : < Group.X, Group.y > + testing set of x and y values + ''' + # Train the model + trainset = reshapeXY(trainset,channels=self.input_channels) + train_dataset=Data(*trainset) + + train_loader = torch.utils.data.DataLoader(dataset = train_dataset, + batch_size = self.batch_size, + shuffle = True) + + model = self.neuralnet(self.num_classes).to(device) + + + #Setting the optimizer with the model parameters and learning rate + optimizer = torch.optim.Adam(model.parameters(), lr=self.learning_rate) + + #this is defined to print how many steps are remaining when training + total_step = len(train_loader) + + + # Training the model + for epoch in range(self.num_epochs): + for i, (images, labels) in enumerate(train_loader): + images = images.to(device) + labels = labels.to(device) + + #Forward pass + outputs = model(images) + loss = self.cost(outputs, labels) + + # Backward and optimize + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if (i+1) % 400 == 0: + print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' + .format(epoch+1, self.num_epochs, i+1, total_step, loss.item())) + + + # Test the model + # In test phase, we don't need to compute gradients (for memory efficiency) + + testset = reshapeXY(testset,channels=self.input_channels) + + test_dataset=Data(*testset) + + test_loader = torch.utils.data.DataLoader(dataset = test_dataset, + batch_size = self.batch_size, + shuffle = True) + + + with torch.no_grad(): + correct = 0 + total = 0 + for images, labels in test_loader: + images = images.to(device) + labels = labels.to(device) + outputs = model(images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + + print('predicted: {} ground-truth: {}'.format(predicted,labels )) + + print('Accuracy of the network on the test data: {} %'.format(100 * correct / total)) + diff --git a/mlbands/__init__.py b/mlbands/__init__.py new file mode 100644 index 00000000..e7d486ae --- /dev/null +++ b/mlbands/__init__.py @@ -0,0 +1,4 @@ +# from mlbands.secret import * # remove (import error ) +from mlbands.main import * +from mlbands.misc import * +from mlbands.ML import * diff --git a/mlbands/main.py b/mlbands/main.py new file mode 100644 index 00000000..68311aa7 --- /dev/null +++ b/mlbands/main.py @@ -0,0 +1,350 @@ +from mp_api.client import MPRester +from pymatgen.analysis.diffraction.xrd import XRDCalculator +from pymatgen.symmetry.analyzer import SpacegroupAnalyzer + +# from mendeleev import element +# elements = {} +# for i in range(1,119): +# elements[element(i).symbol] = i +elements={'H': 1, 'He': 2, 'Li': 3, 'Be': 4, 'B': 5, 'C': 6, 'N': 7, 'O': 8, 'F': 9, 'Ne': 10, 'Na': 11, 'Mg': 12, 'Al': 13, 'Si': 14, 'P': 15, 'S': 16, 'Cl': 17, 'Ar': 18, 'K': 19, 'Ca': 20, 'Sc': 21, 'Ti': 22, 'V': 23, 'Cr': 24, 'Mn': 25, 'Fe': 26, 'Co': 27, 'Ni': 28, 'Cu': 29, 'Zn': 30, 'Ga': 31, 'Ge': 32, 'As': 33, 'Se': 34, 'Br': 35, 'Kr': 36, 'Rb': 37, 'Sr': 38, 'Y': 39, 'Zr': 40, 'Nb': 41, 'Mo': 42, 'Tc': 43, 'Ru': 44, 'Rh': 45, 'Pd': 46, 'Ag': 47, 'Cd': 48, 'In': 49, 'Sn': 50, 'Sb': 51, 'Te': 52, 'I': 53, 'Xe': 54, 'Cs': 55, 'Ba': 56, 'La': 57, 'Ce': 58, 'Pr': 59, 'Nd': 60, 'Pm': 61, 'Sm': 62, 'Eu': 63, 'Gd': 64, 'Tb': 65, 'Dy': 66, 'Ho': 67, 'Er': 68, 'Tm': 69, 'Yb': 70, 'Lu': 71, 'Hf': 72, 'Ta': 73, 'W': 74, 'Re': 75, 'Os': 76, 'Ir': 77, 'Pt': 78, 'Au': 79, 'Hg': 80, 'Tl': 81, 'Pb': 82, 'Bi': 83, 'Po': 84, 'At': 85, 'Rn': 86, 'Fr': 87, 'Ra': 88, 'Ac': 89, 'Th': 90, 'Pa': 91, 'U': 92, 'Np': 93, 'Pu': 94, 'Am': 95, 'Cm': 96, 'Bk': 97, 'Cf': 98, 'Es': 99, 'Fm': 100, 'Md': 101, 'No': 102, 'Lr': 103, 'Rf': 104, 'Db': 105, 'Sg': 106, 'Bh': 107, 'Hs': 108, 'Mt': 109, 'Ds': 110, 'Rg': 111, 'Cn': 112, 'Nh': 113, 'Fl': 114, 'Mc': 115, 'Lv': 116, 'Ts': 117, 'Og': 118} + +from mlbands.misc import * + +import numpy as np +import matplotlib.pyplot as plt + +class Material: + def __init__( self, api_key, material_ID = 'mp-1103503', box_array = None): + '''Material class. To store and process material data + + Parameters + ---------- + API_KEY : str + secret API KEY used to run MPRester data requests + material_ID : int + ID of selected material (without "mp-" prefix) + box_array : (float, float, float) + 3-D Tensor representing material chemical env. + ''' + self.API_KEY = api_key + self.material_ID = material_ID + self.box_array = box_array # material already transformed to box data form (optional) + + def bands(self,nonzero_gap=False): + '''obtain band gap information for requested Material + + Parameters + ---------- + nonzero_gap: bool + if True, only returns nonzero band gaps + ''' + with MPRester(api_key=self.API_KEY) as mpr: + #adapted from https://matsci.org/t/obtain-large-numbers-of-band-structures/3780 + bandstructure = None + try: + bandstructure = mpr.get_bandstructure_by_material_id(self.material_ID,line_mode=False) + except: + pass + if bandstructure: + band_gap = bandstructure.get_band_gap() + print('Band Gap: {} eV\nDirect Gap: {}\nMetallic: {}'.\ + format(band_gap['energy'],\ + 'Yes' if band_gap['direct'] else 'No',\ + 'No' if band_gap['transition'] else 'Yes')) + + if nonzero_gap: + if band_gap['energy']: return band_gap + else: return 0 + else: return band_gap + else: + return 0 + + def load_structure(self, conventional=True): + '''helper function to load structural information of Material + + Parameters + ---------- + conventional: bool + if True, returns conventional standard structure else returns primitive + ''' + with MPRester(self.API_KEY) as mpr: + + # first retrieve the relevant structure + structure = mpr.get_structure_by_material_id(self.material_ID) + + # important to use the conventional structure to ensure + # that peaks are labelled with the conventional Miller indices + sga = SpacegroupAnalyzer(structure) + conventional_structure = sga.get_conventional_standard_structure() + + if conventional: + structure = conventional_structure + + return structure + + + def structural(self): + '''prints structural information + ''' + structure = Material(self.API_KEY, self.material_ID).load_structure() + + print(structure.lattice) + print(structure.sites) #https://pymatgen.org/pymatgen.core.sites.html?highlight=periodicsite#pymatgen.core.sites.PeriodicSite + + Nsites = len(structure.sites) + for i in range(Nsites): + print('\n\n') + # print(structure.sites[i]) + print(structure.sites[i].species) + print(structure.sites[i].coords) + print(structure.sites[i].frac_coords) + + + def to_xyz(self, fractional=False): + '''processes the material structure as an array with an xyz coordinate format + i.e. [atom_numbers, x_coord, y_coord, z_coord] + + Parameters + ---------- + fractional: bool + if False, returns xyz coordinates else returns fractional (a,b,c) coordinates + ''' + structure = Material(self.API_KEY, self.material_ID).load_structure(conventional=True) + + Nsites = len(structure.sites) + + xyz_array = np.zeros((Nsites,4)) + + if not fractional: + for i in range(Nsites): + # atom_num = elements(str(structure.sites[i].specie)).atomic_number # specie is not a typo + atom_num = elements[str(structure.sites[i].specie)] # specie is not a typo + + xyz_array[i] = [atom_num,*structure.sites[i].coords] + else: + for i in range(Nsites): + # atom_num = elements(str(structure.sites[i].specie)).atomic_number + atom_num = elements[str(structure.sites[i].specie)] + + xyz_array[i] = [atom_num,*structure.sites[i].frac_coords] + + return xyz_array + + + def to_box(self, fractional=False): + '''processes the material structure as a sparse, 3-D Tensor "Box" with atom_numbers as the + non-zero entries + i.e. box = Tensor((z_coord, y_coord, x_coord)) + + Parameters + ---------- + fractional: bool + if False, returns tensor space from xyz coordinates else from fractional (a,b,c) coordinates + ''' + xyz_array = Material(self.API_KEY, self.material_ID).to_xyz(fractional) + + coords = xyz_array[:,1:] + MAX = np.ceil(np.max(coords)).astype('int') + MIN = np.floor(np.min(coords)).astype('int') + + xyz_array[:,1:] -= MIN + + L = (MAX-MIN+1) + + box = np.zeros((L,L,L)) + for i in xyz_array: + print(i) + + atom,x,y,z = i.astype('int') + box[z,y,x] = atom + + return box + + def visual(self, spacing = 1, fractional=False): + '''simple visualization tool for Material (plot) + + Parameters + ---------- + spacing: float + increases spacing by multiplying coordinate distance + fractional: bool + if False, returns tensor space from xyz coordinates else from fractional (a,b,c) coordinates + ''' + ax = plt.axes(projection='3d') + colors = np.linspace(2**20,2**24,118,dtype='int') #divide color range into 118 colors (for the 118 chemical elements) + + + if self.box_array is not None: + #presence of box_array supplants material_ID + for i in np.argwhere(self.box_array): + x,y,z = i + atom = int(self.box_array[tuple(i)]) + ax.scatter3D(x,y,z, s=100, c="#"+hex(colors[atom])[2:]) + + else: + xyz_array = Material(self.API_KEY, self.material_ID).to_xyz(fractional) + xyz_array[:,1:]*=spacing + + for i in xyz_array: + atom,*xyz = i.astype('int') + ax.scatter3D(*xyz, s=100, c="#"+hex(colors[atom])[2:]) + + + set_axes_equal(ax) + + plt.axis('off') + plt.show() + + + def XRD(self): + '''XRD pattern data for Material + ''' + structure = Material(self.API_KEY, self.material_ID).load_structure(self.API_KEY) + + # this example shows how to obtain an XRD diffraction pattern + # these patterns are calculated on-the-fly from the structure + calculator = XRDCalculator(wavelength='CuKa') + pattern = calculator.get_pattern(structure) + + print('\npattern:\n',pattern) + + + def thermo(self): + '''thermodynamic data for Material + ''' + with MPRester(api_key=self.API_KEY) as mpr: + + # for a single material + # thermo_docs = mpr.thermo.get_data_by_id(self.material_ID) # DOES NOT WORK msg(Item with thermo_id = mp-1103503 not found) + + # for many materials, it's much faster to use + # the `search` method, where additional material_ids can + # be added to this list + thermo_docs = mpr.thermo.search(material_ids=[self.material_ID]) + + # print(thermo_docs[0].energy_per_atom) + return thermo_docs + + def magnetism(self): + '''magnetic properties data for Material + ''' + with MPRester(api_key=self.API_KEY) as mpr: + magnetism_doc = mpr.magnetism.get_data_by_id('mp-1103503') + + # print(magnetism_doc.total_magnetization) + return magnetism_doc + +class Group: + def __init__(self,api_key): + '''Group class for bulk calculations + + Parameters + ---------- + API_KEY : str + secret API KEY used to run MPRester data requests + materials : int + IDs of selected material (with "mp-" prefix) + X : [varies] (float,float,float); ((float,float,float), *float); (*float) + quantitative predictors ("X-value") + Y : float + band gap (eV) quantitative response ("Y-value","label") + ''' + self.API_KEY = api_key + self.materials = [] # material IDs (materials found in ID_list; see data_make inputs) + self.X = [] # quantitative predictors ("X-value") + self.Y = [] # quantitative response ("Y-value","label") + + self.box_lengths = [] # length scale of boxes (3-D Tensors of material chemical env.) + self.max_length = 0 + + def transfer(self, loaded_data): + '''transfer constructor variable information to Group class from loaded data + + Parameters + ---------- + loaded data : <"Group" class object> + file containing processed Materials in a Group container + ''' + self.X = loaded_data.X + self.Y = loaded_data.Y + self.materials = loaded_data.materials + + def data_make(self, ID_list = range(1,10), nonzero_gap=False ): + '''generate Group data for selected ID_list numbers [ for materials which exist with such IDs ] + + Parameters + ---------- + ID_list : list[int] + list of material_ID numbers (i.e. without "mp-" prefix) + nonzero_gap: bool + if True, only returns nonzero band gaps + ''' + for i in ID_list: + material = Material(self.API_KEY, 'mp-'+str(i)) + BG = material.bands(nonzero_gap) + if BG: # if material with material_ID exists + self.materials.append('mp-'+str(i)) + + # quantitative response ("Y-value","label") + self.Y.append(BG['energy']) # append band gap energy ( eV ) + + box = material.to_box() + self.box_lengths.append(box.shape[0]) + # quantitative predictors ("X-value") + self.X.append(box) # append 3-D Tensor representing material chemical env. + # for extra_x in features: + # self.X.append(extra_x) + + self.Y = np.array(self.Y) + + def data_expand(self,boxes=True,*property_funcs): + '''expand "X-values" data to other chemical characteristics + + e.g. data_expand(True, thermo, magnetic) appends "thermo" and "magnetic" characteristics to X in addition to 3-D Tensors + e.g. data_expand(False, thermo, magnetic) creates a new X-value object and appends "thermo" and "magnetic" characteristics alone + + Parameters + ---------- + boxes : bool + removes 3-D tensors to X variable if False, + *property_funcs: , ..., + operates functions on Materials which extract additional properties and appends them to X + ''' + boxes = self.X + self.X = [] + # load a property for all materials + for i in self.materials: + material = Material(self.API_KEY, 'mp-'+str(i)) + + k=0 + material_props = [] + material_props.append(boxes[k]) if boxes else None + for func in property_funcs: + material_props.append(material.func) + + self.X.append(material_props) + k+=1 + + + def resize_boxes(self, L=32): + '''resize 3-D tensor boxes to fit neural network architecture (32x32x32) + + Parameters + ---------- + L: int + length-scale of boxes (3-D Tensors representing material chemical env.) + ''' + max_length = np.max(self.box_lengths) + + if L >= max_length: + + + self.X = np.array([ np.pad(self.X[i],\ + ( (0,L-self.box_lengths[i]),(0,L-self.box_lengths[i]),(0,L-self.box_lengths[i]) ) + ) for i in range(len(self.X)) ]) + + # print(self.X.shape) + + + + diff --git a/mlbands/misc.py b/mlbands/misc.py new file mode 100644 index 00000000..ec573cb8 --- /dev/null +++ b/mlbands/misc.py @@ -0,0 +1,35 @@ +import numpy as np + +def set_axes_radius(ax, origin, radius): + '''set_axes_radius and set_axes_equal * * * Credit: + Mateen Ulhaq (answered Jun 3 '18 at 7:55) + https://stackoverflow.com/questions/13685386/matplotlib-equal-unit-length-with-equal-aspect-ratio-z-axis-is-not-equal-to''' + ax.set_xlim3d([origin[0] - radius, origin[0] + radius]) + ax.set_ylim3d([origin[1] - radius, origin[1] + radius]) + ax.set_zlim3d([origin[2] - radius, origin[2] + radius]) +def set_axes_equal(ax): + '''Make axes of 3D plot have equal scale so that spheres appear as spheres, + cubes as cubes, etc.. This is one possible solution to Matplotlib's + ax.set_aspect('equal') and ax.axis('equal') not working for 3D. + Input + ax: a matplotlib axis, e.g., as output from plt.gca(). + ''' + limits = np.array([ + ax.get_xlim3d(), + ax.get_ylim3d(), + ax.get_zlim3d(), + ]) + + origin = np.mean(limits, axis=1) + radius = 0.5 * np.max(np.abs(limits[:, 1] - limits[:, 0])) + set_axes_radius(ax, origin, radius) + + +# adapted from https://stackoverflow.com/a/47381855/14460178 +import pickle + +def save(data, filename = 'file.mat3r'): + with open(filename, 'wb') as handle: pickle.dump(data, handle) + +def load(filename = 'file.mat3r'): + with open(filename, 'rb') as handle: return pickle.load(handle) diff --git a/mlbands/neuralnets/__init__.py b/mlbands/neuralnets/__init__.py new file mode 100644 index 00000000..8a3063ec --- /dev/null +++ b/mlbands/neuralnets/__init__.py @@ -0,0 +1,2 @@ +from .lenet5 import LeNet5 +from .lenet3d import LeNet3D diff --git a/mlbands/neuralnets/lenet3d.py b/mlbands/neuralnets/lenet3d.py new file mode 100644 index 00000000..2f549f58 --- /dev/null +++ b/mlbands/neuralnets/lenet3d.py @@ -0,0 +1,47 @@ +# https://github.com/andrewrgarcia/3D-LeNet-with-PyTorch +from torch.autograd import Variable +import torch +import torch.nn as nn +import torch.nn.functional as F + +L=32 # length scale of cubes (default: 32 x 32 x 32) + +class LeNet3D(nn.Module): + def __init__(self, num_classes): + super(LeNet3D, self).__init__() + + self.conv1 = nn.Conv3d(1, 6, kernel_size=(5, 5, 5)) + self.pool = nn.MaxPool3d(2, 2) + self.conv2 = nn.Conv3d(6, (L//2), kernel_size=(5, 5, 5)) + self.fc1 = nn.Linear((L//2) * 5 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, num_classes) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + # print(x.size()) + x = self.pool(F.relu(self.conv2(x))) + # print(x.size()) + x = x.view(-1, (L//2) * 5 * 5 * 5) + # print(x.size()) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +# # input: (N = batch_size, C = 1, L = 32, L = 32, L = 32) +# # output: (N, num_classes) + +# # Test the model: +# num_classes = 1 + +# model = LeNet3D(num_classes) + +# x = Variable(torch.randn(10, 1, L, L, L)) # (N_samples,C_channels,D=L,H=L,W=L) +# print(x) +# y = model(x) +# print(y) + + + diff --git a/mlbands/neuralnets/lenet5.py b/mlbands/neuralnets/lenet5.py new file mode 100644 index 00000000..3acaa5aa --- /dev/null +++ b/mlbands/neuralnets/lenet5.py @@ -0,0 +1,33 @@ +# LeNet5 from Paperspace blog https://blog.paperspace.com/writing-lenet5-from-scratch-in-python/ +import torch.nn as nn + +#Defining the convolutional neural network +class LeNet5(nn.Module): + def __init__(self, num_classes): + super(LeNet5, self).__init__() + self.layer1 = nn.Sequential( + nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0), + nn.BatchNorm2d(6), + nn.ReLU(), + nn.MaxPool2d(kernel_size = 2, stride = 2)) + self.layer2 = nn.Sequential( + nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0), + nn.BatchNorm2d(16), + nn.ReLU(), + nn.MaxPool2d(kernel_size = 2, stride = 2)) + self.fc = nn.Linear(400, 120) + self.relu = nn.ReLU() + self.fc1 = nn.Linear(120, 84) + self.relu1 = nn.ReLU() + self.fc2 = nn.Linear(84, num_classes) + + def forward(self, x): + out = self.layer1(x) + out = self.layer2(out) + out = out.reshape(out.size(0), -1) + out = self.fc(out) + out = self.relu(out) + out = self.fc1(out) + out = self.relu1(out) + out = self.fc2(out) + return out \ No newline at end of file diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 00000000..8e2f0f82 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,1397 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "certifi" +version = "2022.12.7" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"}, + {file = "certifi-2022.12.7.tar.gz", hash = "sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3"}, +] + +[[package]] +name = "charset-normalizer" +version = "2.1.1" +description = "The Real First Universal Charset Detector. 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"mlbands" +version = "1.0.1" +description = "A Python package that implements automatic prediction of electronic band gaps for a set of materials based on training data" +authors = ["Andrew R. Garcia"] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.8" +mp-api = "^0.30.5" +torch = "^1.13.1" +torchvision = "^0.14.1" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_module.py b/tests/test_module.py new file mode 100644 index 00000000..2a32c751 --- /dev/null +++ b/tests/test_module.py @@ -0,0 +1,82 @@ +import mlbands + +# replace below line with API key [ in string form ] from Materials Project site (https://materialsproject.org/api#api-key) +SECRET_KEY = '' + +def test_materialprops(): + material = mlbands.Material(SECRET_KEY, 'mp-1103503') + + material.structural() + material.XRD() + material.thermo() + totalmag = material.magnetism().total_magnetization + print(totalmag) + +def test_visuals(): + material = mlbands.Material(SECRET_KEY, 'mp-1103502') + box = material.to_box(True) + material.visual(10,True) + box = material.to_box() + material.visual() + + material = mlbands.Material(SECRET_KEY, 'mp-1103506') + box = material.to_box(True) + material.visual(10,True) + +def test_loadvisual(): + xdata = mlbands.load('materials.data') + print(xdata) + + mlbands.Material(SECRET_KEY, box_array = xdata[3]).visual() + mlbands.Material(SECRET_KEY).visual() + + +def test_bands(): + + training = mlbands.Group(SECRET_KEY) + training.data_make(range(1,100)) + # training.data_make(range(1,30),True) + training.resize_boxes() + + testing = mlbands.Group(SECRET_KEY) + testing.data_make(range(300,350)) + # testing.data_make(range(300,314),True) + testing.resize_boxes() + + machine = mlbands.Machine() + machine.learn([training.X,training.Y],[testing.X,testing.Y]) + + mlbands.save(training, 'train.data') + mlbands.save(testing, 'test.data') + +def test_bands_load(): + + training = mlbands.load('train.data') + trainXY = [training.X,training.Y] + testing = mlbands.load('test.data') + testXY = [testing.X,testing.Y] + + machine = mlbands.Machine() + machine.learn(trainXY,testXY) + +def test_dataexpand(): + + training = mlbands.Group(SECRET_KEY) + traindata = mlbands.load('train.data') + training.transfer(traindata) + + testing = mlbands.load('test.data') + testdata = mlbands.load('train.data') + testing.transfer(testdata) + + print(training.X) + print(testing.materials) + + + +test_materialprops() +test_visuals() +test_loadvisual() +test_dataexpand() +# test_bands() +test_bands_load()