diff --git a/README.md b/README.md
index bbde080d..c2659bbc 100644
--- a/README.md
+++ b/README.md
@@ -81,9 +81,9 @@ This package relies on quantum defects provided by the community. Consider citin
 
 
 ## Using custom quantum defects
-To use custom quantum defects (or quantum defects for a new element), you can simply create a subclass of `ryd_numerov.elements.base_element.BaseElement` (e.g. `class CustomRubidium(BaseElement):`) with a custom species name (e.g. `species = "Custom_Rb"`).
-Then, similarly to `ryd_numerov.elements.rubidium.py` you can define the quantum defects (and model potential parameters, ...) for your element.
-Finally, you can use the custom element by simply calling `ryd_numerov.RydbergStateAlkali("Custom_Rb", n=50, l=0, j=1/2, m=1/2)` (the code will look for all subclasses of `BaseElement` until it finds one with the species name "Custom_Rb").
+To use custom quantum defects (or quantum defects for a new species), you can simply create a subclass of `ryd_numerov.species.species_object.SpeciesObject` (e.g. `class CustomRubidium(SpeciesObject):`) with a custom species name (e.g. `name = "Custom_Rb"`).
+Then, similarly to `ryd_numerov.species.rubidium.py` you can define the quantum defects (and model potential parameters, ...) for your species.
+Finally, you can use the custom species by simply calling `ryd_numerov.RydbergStateAlkali("Custom_Rb", n=50, l=0, j=1/2, m=1/2)` (the code will look for all subclasses of `SpeciesObject` until it finds one with the species name "Custom_Rb").
 
 
 ## License
diff --git a/docs/examples/comparisons/compare_nist_energy_levels_data.ipynb b/docs/examples/comparisons/compare_nist_energy_levels_data.ipynb
index 7e7d305a..5fb34615 100644
--- a/docs/examples/comparisons/compare_nist_energy_levels_data.ipynb
+++ b/docs/examples/comparisons/compare_nist_energy_levels_data.ipynb
@@ -19,41 +19,40 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "from ryd_numerov.elements import Strontium88"
+    "from ryd_numerov.species import Strontium88"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "element = Strontium88(use_nist_data=True, nist_n_max=60)\n",
-    "element_without_nist = Strontium88(use_nist_data=False)\n",
+    "species = Strontium88()\n",
     "s_tot = 0  # singlet\n",
     "\n",
-    "energies_with_nist = []\n",
-    "energies_without_nist = []\n",
+    "nus_with_nist = []\n",
+    "nus_without_nist = []\n",
     "labels = []\n",
     "n_list = []\n",
     "\n",
     "l_int2str = {0: \"s\", 1: \"p\", 2: \"d\", 3: \"f\", 4: \"g\", 5: \"h\", 6: \"i\", 7: \"j\"}\n",
     "for n in range(5, 25):\n",
     "    for l in range(n):\n",
-    "        if not element.is_allowed_shell(n, l, s_tot):\n",
+    "        if not species.is_allowed_shell(n, l, s_tot):\n",
     "            continue\n",
     "        for _j_tot in np.arange(abs(l - s_tot), l + s_tot + 1):\n",
     "            j_tot = float(_j_tot)\n",
-    "            if (n, l, j_tot, s_tot) not in element._nist_energy_levels:  # noqa: SLF001\n",
+    "            if (n, l, j_tot, s_tot) not in species._nist_energy_levels:  # noqa: SLF001\n",
     "                continue\n",
     "\n",
     "            labels.append(f\"{n}{l_int2str.get(l, ',' + str(l))}_{j_tot:.1f}\")\n",
-    "            energies_with_nist.append(element.calc_energy(n, l, j_tot, s_tot, unit=\"hartree\"))\n",
-    "            energies_without_nist.append(element_without_nist.calc_energy(n, l, j_tot, s_tot, unit=\"hartree\"))\n",
+    "            nus_with_nist.append(species.calc_nu(n, l, j_tot, s_tot, use_nist_data=True, nist_n_max=60))\n",
+    "            nus_without_nist.append(species.calc_nu(n, l, j_tot, s_tot, use_nist_data=False))\n",
     "            n_list.append(n)\n",
     "\n",
-    "energies_with_nist = np.array(energies_with_nist) + element.get_ionization_energy(unit=\"hartree\")\n",
-    "energies_without_nist = np.array(energies_without_nist) + element_without_nist.get_ionization_energy(unit=\"hartree\")"
+    "nus_with_nist = np.array(nus_with_nist)\n",
+    "nus_without_nist = np.array(nus_without_nist)"
    ]
   },
   {
@@ -63,7 +62,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -75,26 +74,19 @@
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
-    "ax.set_ylabel(\"Energy\")\n",
-    "if False:  # Change to True if you want to scale energies by n^2\n",
-    "    energies_with_nist *= np.array(n_list) ** 2\n",
-    "    energies_without_nist *= np.array(n_list) ** 2\n",
-    "    ax.set_ylabel(\"Energy * n^2\")\n",
-    "\n",
     "for i, label in enumerate(labels):\n",
-    "    if energies_with_nist[i] < 0 or energies_without_nist[i] < 0:\n",
-    "        print(\n",
-    "            f\"Skipping negative energy for {label}: \"\n",
-    "            f\"{energies_with_nist[i]} (with NIST), \"\n",
-    "            f\"{energies_without_nist[i]} (without NIST)\"\n",
-    "        )\n",
-    "        continue\n",
-    "    ax.plot([0, 1], [energies_with_nist[i]] * 2, \"C0\", zorder=-10)\n",
-    "    ax.plot([0, 1], [energies_without_nist[i]] * 2, \"C3--\", zorder=10)\n",
-    "    ax.text(1.05, energies_with_nist[i], label, color=\"C0\")\n",
-    "    ax.text(-0.05, energies_without_nist[i], label, color=\"C3\", ha=\"right\")\n",
+    "    ax.plot([0, 1], [nus_with_nist[i]] * 2, \"C0\", zorder=-10)\n",
+    "    ax.plot([0, 1], [nus_without_nist[i]] * 2, \"C3--\", zorder=10)\n",
+    "    ax.text(1.05, nus_with_nist[i], label, color=\"C0\")\n",
+    "    ax.text(-0.05, nus_without_nist[i], label, color=\"C3\", ha=\"right\")\n",
+    "\n",
+    "ax.plot([], [] * 2, \"C0\", label=\"NIST data\")\n",
+    "ax.plot([], [] * 2, \"C3--\", label=\"Quantum defect formula\")\n",
+    "ax.legend(loc=\"upper right\")\n",
     "\n",
+    "ax.set_ylabel(r\"Effective principal quantum number $\\nu$\")\n",
     "ax.set_xlim(-0.25, 1.25)\n",
+    "ax.set_ylim(0, 15)\n",
     "\n",
     "plt.show()"
    ]
@@ -106,7 +98,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -116,13 +108,13 @@
     }
    ],
    "source": [
-    "energies_diff = np.abs(np.array(energies_with_nist) - np.array(energies_without_nist))\n",
-    "\n",
     "fig, ax = plt.subplots()\n",
-    "ax.bar(np.arange(len(energies_diff)), energies_diff, tick_label=labels, color=\"C0\")\n",
-    "ax.set_ylabel(\"Energy difference (Hartree)\")\n",
-    "ax.set_xticklabels(labels, rotation=90)\n",
     "\n",
+    "nus_diff = np.abs(np.array(nus_with_nist) - np.array(nus_without_nist))\n",
+    "ax.bar(np.arange(len(nus_diff)), nus_diff, tick_label=labels, color=\"C0\")\n",
+    "\n",
+    "ax.set_xticklabels(labels, rotation=90)\n",
+    "ax.set_ylabel(r\"$\\Delta \\nu = \\left|\\nu_{\\text{NIST}} - \\nu_{\\text{QuantumDefect}}\\right|$\")\n",
     "ax.set_yscale(\"log\")\n",
     "\n",
     "fig.tight_layout()\n",
diff --git a/pyproject.toml b/pyproject.toml
index 30263ad6..5812ae11 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -90,7 +90,7 @@ version = {attr = "ryd_numerov.__version__"}
 where = ["src"]
 
 [tool.setuptools.package-data]
-ryd_numerov = ["elements/nist_energy_levels/*.txt"]
+ryd_numerov = ["species/nist_energy_levels/*.txt"]
 
 [tool.check-wheel-contents]
 toplevel = ["ryd_numerov"]
diff --git a/src/ryd_numerov/__init__.py b/src/ryd_numerov/__init__.py
index b4cb1356..b26c1b1b 100644
--- a/src/ryd_numerov/__init__.py
+++ b/src/ryd_numerov/__init__.py
@@ -1,4 +1,4 @@
-from ryd_numerov import angular, elements, radial
+from ryd_numerov import angular, radial, species
 from ryd_numerov.rydberg_state import RydbergStateAlkali, RydbergStateAlkaliHyperfine, RydbergStateAlkalineLS
 from ryd_numerov.units import ureg
 
@@ -7,8 +7,8 @@
     "RydbergStateAlkaliHyperfine",
     "RydbergStateAlkalineLS",
     "angular",
-    "elements",
     "radial",
+    "species",
     "ureg",
 ]
 
diff --git a/src/ryd_numerov/angular/angular_ket.py b/src/ryd_numerov/angular/angular_ket.py
index c4f9a320..e068d9c7 100644
--- a/src/ryd_numerov/angular/angular_ket.py
+++ b/src/ryd_numerov/angular/angular_ket.py
@@ -21,7 +21,7 @@
     minus_one_pow,
     try_trivial_spin_addition,
 )
-from ryd_numerov.elements import BaseElement
+from ryd_numerov.species import SpeciesObject
 
 if TYPE_CHECKING:
     from ryd_numerov.angular.angular_state import AngularState
@@ -86,7 +86,7 @@ def __init__(
         l_r: int | None = None,
         f_tot: float | None = None,  # noqa: ARG002
         m: float | None = None,
-        species: str | None = None,
+        species: str | SpeciesObject | None = None,
     ) -> None:
         """Initialize the Spin ket.
 
@@ -95,11 +95,12 @@ def __init__(
         Not used for calculation, only for convenience to infer the core electron spin and nuclear spin quantum numbers.
         """
         if species is not None:
-            element = BaseElement.from_species(species)
-            if i_c is not None and i_c != element.i_c:
-                raise ValueError(f"Nuclear spin i_c={i_c} does not match the element {species} with i_c={element.i_c}.")
-            i_c = element.i_c
-            s_c = 0.5 * (element.number_valence_electrons - 1)
+            if isinstance(species, str):
+                species = SpeciesObject.from_name(species)
+            if i_c is not None and i_c != species.i_c:
+                raise ValueError(f"Nuclear spin i_c={i_c} does not match the species {species} with i_c={species.i_c}.")
+            i_c = species.i_c
+            s_c = 0.5 * (species.number_valence_electrons - 1)
         if i_c is None:
             raise ValueError("Nuclear spin i_c must be set or a species must be given.")
         self.i_c = float(i_c)
@@ -548,7 +549,7 @@ def __init__(
         j_tot: float | None = None,
         f_tot: float | None = None,
         m: float | None = None,
-        species: str | None = None,
+        species: str | SpeciesObject | None = None,
     ) -> None:
         """Initialize the Spin ket."""
         super().__init__(i_c, s_c, l_c, s_r, l_r, f_tot, m, species)
@@ -611,7 +612,7 @@ def __init__(
         j_tot: float | None = None,
         f_tot: float | None = None,
         m: float | None = None,
-        species: str | None = None,
+        species: str | SpeciesObject | None = None,
     ) -> None:
         """Initialize the Spin ket."""
         super().__init__(i_c, s_c, l_c, s_r, l_r, f_tot, m, species)
@@ -674,7 +675,7 @@ def __init__(
         j_r: float | None = None,
         f_tot: float | None = None,
         m: float | None = None,
-        species: str | None = None,
+        species: str | SpeciesObject | None = None,
     ) -> None:
         """Initialize the Spin ket."""
         super().__init__(i_c, s_c, l_c, s_r, l_r, f_tot, m, species)
diff --git a/src/ryd_numerov/elements/__init__.py b/src/ryd_numerov/elements/__init__.py
deleted file mode 100644
index 5f9a2a66..00000000
--- a/src/ryd_numerov/elements/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from ryd_numerov.elements.base_element import BaseElement
-from ryd_numerov.elements.cesium import Cesium
-from ryd_numerov.elements.hydrogen import Hydrogen, HydrogenTextBook
-from ryd_numerov.elements.lithium import Lithium
-from ryd_numerov.elements.potassium import Potassium
-from ryd_numerov.elements.rubidium import Rubidium
-from ryd_numerov.elements.sodium import Sodium
-from ryd_numerov.elements.strontium import Strontium87, Strontium88
-from ryd_numerov.elements.ytterbium import Ytterbium171, Ytterbium173, Ytterbium174
-
-__all__ = [
-    "BaseElement",
-    "Cesium",
-    "Hydrogen",
-    "HydrogenTextBook",
-    "Lithium",
-    "Potassium",
-    "Rubidium",
-    "Sodium",
-    "Strontium87",
-    "Strontium88",
-    "Ytterbium171",
-    "Ytterbium173",
-    "Ytterbium174",
-]
diff --git a/src/ryd_numerov/radial/model.py b/src/ryd_numerov/radial/model.py
index 71b43c69..17562c33 100644
--- a/src/ryd_numerov/radial/model.py
+++ b/src/ryd_numerov/radial/model.py
@@ -6,7 +6,7 @@
 
 import numpy as np
 
-from ryd_numerov.elements import BaseElement
+from ryd_numerov.species import SpeciesObject
 
 if TYPE_CHECKING:
     from ryd_numerov.units import NDArray
@@ -24,7 +24,7 @@ class Model:
 
     def __init__(
         self,
-        species: str,
+        species: str | SpeciesObject,
         l: int,
         potential_type: PotentialType | None = None,
     ) -> None:
@@ -36,11 +36,13 @@ def __init__(
             potential_type: Which potential to use for the model.
 
         """
-        self.element = BaseElement.from_species(species)
+        if isinstance(species, str):
+            species = SpeciesObject.from_name(species)
+        self.species = species
         self.l = l
 
         if potential_type is None:
-            potential_type = self.element.potential_type_default
+            potential_type = self.species.potential_type_default
             if potential_type is None:
                 potential_type = "coulomb"
         if potential_type not in get_args(PotentialType):
@@ -91,23 +93,23 @@ def calc_model_potential_marinescu_1993(self, x: XType) -> XType:
             V_{mp,marinescu}: The four parameter potential V_{mp,marinescu}(x) in atomic units.
 
         """
-        parameter_dict = self.element.model_potential_parameter_marinescu_1993
+        parameter_dict = self.species.model_potential_parameter_marinescu_1993
         if len(parameter_dict) == 0:
-            raise ValueError("No parametric model potential parameters defined for this element.")
+            raise ValueError(f"No parametric model potential parameters defined for the species {self.species}.")
         # default to parameters for the maximum l
         a1, a2, a3, a4 = parameter_dict.get(self.l, parameter_dict[max(parameter_dict.keys())])
         exp_a1 = np.exp(-a1 * x)
         exp_a2 = np.exp(-a2 * x)
-        z_nl: XType = 1 + (self.element.Z - 1) * exp_a1 - x * (a3 + a4 * x) * exp_a2
+        z_nl: XType = 1 + (self.species.Z - 1) * exp_a1 - x * (a3 + a4 * x) * exp_a2
         v_c = -z_nl / x
 
-        alpha_c = self.element.alpha_c_marinescu_1993
+        alpha_c = self.species.alpha_c_marinescu_1993
         if alpha_c == 0:
             v_p = 0
         else:
-            r_c_dict = self.element.r_c_dict_marinescu_1993
+            r_c_dict = self.species.r_c_dict_marinescu_1993
             if len(r_c_dict) == 0:
-                raise ValueError("No parametric model potential parameters defined for this element.")
+                raise ValueError(f"No parametric model potential parameters defined for the species {self.species}.")
             # default to x_c for the maximum l
             x_c = r_c_dict.get(self.l, r_c_dict[max(r_c_dict.keys())])
             x2: XType = x * x
@@ -137,10 +139,10 @@ def calc_model_potential_fei_2009(self, x: XType) -> XType:
             V_{mp,fei}: The four parameter potential V_{mp,fei}(x) in atomic units.
 
         """
-        delta, alpha, beta, gamma = self.element.model_potential_parameter_fei_2009
+        delta, alpha, beta, gamma = self.species.model_potential_parameter_fei_2009
         with np.errstate(over="ignore"):
             denom: XType = 1 - alpha + alpha * np.exp(beta * x**delta + gamma * x ** (2.0 * delta))
-            return -1 / x - (self.element.Z - 1) / (x * denom)
+            return -1 / x - (self.species.Z - 1) / (x * denom)
 
     def calc_effective_potential_centrifugal(self, x: XType) -> XType:
         r"""Calculate the effective centrifugal potential V_l(x) in atomic units.
@@ -160,7 +162,7 @@ def calc_effective_potential_centrifugal(self, x: XType) -> XType:
 
         """
         x2 = x * x
-        return (1 / self.element.reduced_mass_factor) * self.l * (self.l + 1) / (2 * x2)
+        return (1 / self.species.reduced_mass_au) * self.l * (self.l + 1) / (2 * x2)
 
     def calc_effective_potential_sqrt(self, x: XType) -> XType:
         r"""Calculate the effective potential V_sqrt(x) from the sqrt transformation in atomic units.
@@ -181,7 +183,7 @@ def calc_effective_potential_sqrt(self, x: XType) -> XType:
 
         """
         x2 = x * x
-        return (1 / self.element.reduced_mass_factor) * (3 / 32) / x2
+        return (1 / self.species.reduced_mass_au) * (3 / 32) / x2
 
     def calc_total_effective_potential(self, x: XType) -> XType:
         r"""Calculate the total effective potential V_eff(x) in atomic units.
diff --git a/src/ryd_numerov/radial/radial_state.py b/src/ryd_numerov/radial/radial_state.py
index ed031788..4b87efce 100644
--- a/src/ryd_numerov/radial/radial_state.py
+++ b/src/ryd_numerov/radial/radial_state.py
@@ -6,11 +6,12 @@
 
 import numpy as np
 
-from ryd_numerov.elements import BaseElement
 from ryd_numerov.radial.grid import Grid
 from ryd_numerov.radial.model import Model
 from ryd_numerov.radial.radial_matrix_element import calc_radial_matrix_element_from_w_z
 from ryd_numerov.radial.wavefunction import WavefunctionNumerov, WavefunctionWhittaker
+from ryd_numerov.species import SpeciesObject
+from ryd_numerov.species.utils import calc_energy_from_nu
 from ryd_numerov.units import ureg
 
 if TYPE_CHECKING:
@@ -27,7 +28,7 @@ class RadialState:
 
     def __init__(
         self,
-        species: str,
+        species: str | SpeciesObject,
         nu: float,
         l_r: int,
     ) -> None:
@@ -40,6 +41,8 @@ def __init__(
             l_r: Orbital angular momentum quantum number of the rydberg electron.
 
         """
+        if isinstance(species, str):
+            species = SpeciesObject.from_name(species)
         self.species = species
 
         self.n: int | None = None
@@ -75,7 +78,7 @@ def set_n_for_sanity_check(self, n: int) -> None:
     def __repr__(self) -> str:
         species, nu, l_r, n = self.species, self.nu, self.l_r, self.n
         n_str = "" if n is None else f", ({n=})"
-        return f"{self.__class__.__name__}({species}, {nu=}, {l_r=}{n_str})"
+        return f"{self.__class__.__name__}({species.name}, {nu=}, {l_r=}{n_str})"
 
     def __str__(self) -> str:
         return self.__repr__()
@@ -131,8 +134,7 @@ def create_grid(
             if self.l_r <= 10:
                 z_min = 0.0
             else:
-                element = BaseElement.from_species(self.species)
-                energy_au = element.calc_energy_from_nu(self.nu)
+                energy_au = calc_energy_from_nu(self.species.reduced_mass_au, self.nu)
                 z_min = self.model.calc_turning_point_z(energy_au)
                 z_min = math.sqrt(0.5) * z_min - 3  # see also compare_z_min_cutoff.ipynb
         else:
diff --git a/src/ryd_numerov/radial/wavefunction.py b/src/ryd_numerov/radial/wavefunction.py
index f5822bab..6ada48ff 100644
--- a/src/ryd_numerov/radial/wavefunction.py
+++ b/src/ryd_numerov/radial/wavefunction.py
@@ -9,8 +9,8 @@
 from mpmath import whitw
 from scipy.special import gamma
 
-from ryd_numerov.elements.base_element import BaseElement
 from ryd_numerov.radial.numerov import _run_numerov_integration_python, run_numerov_integration
+from ryd_numerov.species.utils import calc_energy_from_nu
 
 if TYPE_CHECKING:
     from ryd_numerov.radial import Grid, Model
@@ -172,10 +172,10 @@ def integrate(self, run_backward: bool = True, w0: float = 1e-10, *, _use_njit:
         # and not like in the rest of this class, i.e. y = w(z) and x = z
         grid = self.grid
 
-        element = BaseElement.from_species(self.radial_state.species)
-        energy_au = element.calc_energy_from_nu(self.radial_state.nu)
+        species = self.radial_state.species
+        energy_au = calc_energy_from_nu(species.reduced_mass_au, self.radial_state.nu)
         v_eff = self.model.calc_total_effective_potential(grid.x_list)
-        glist = 8 * element.reduced_mass_factor * grid.z_list * grid.z_list * (energy_au - v_eff)
+        glist = 8 * species.reduced_mass_au * grid.z_list * grid.z_list * (energy_au - v_eff)
 
         if run_backward:
             # During the Numerov integration we define the wavefunction such that it should always stop
diff --git a/src/ryd_numerov/rydberg_state.py b/src/ryd_numerov/rydberg_state.py
index 65ce4aee..456db954 100644
--- a/src/ryd_numerov/rydberg_state.py
+++ b/src/ryd_numerov/rydberg_state.py
@@ -10,8 +10,9 @@
 
 from ryd_numerov.angular import AngularKetLS
 from ryd_numerov.angular.utils import try_trivial_spin_addition
-from ryd_numerov.elements.base_element import BaseElement
 from ryd_numerov.radial import RadialState
+from ryd_numerov.species.species_object import SpeciesObject
+from ryd_numerov.species.utils import calc_energy_from_nu
 from ryd_numerov.units import BaseQuantities, MatrixElementType, ureg
 
 if TYPE_CHECKING:
@@ -32,24 +33,11 @@
 
 
 class RydbergStateBase(ABC):
-    species: str
+    species: SpeciesObject
 
     def __str__(self) -> str:
         return self.__repr__()
 
-    @property
-    def element(self) -> BaseElement:
-        """The element of the Rydberg state."""
-        if not hasattr(self, "_element"):
-            self.create_element()
-        return self._element
-
-    def create_element(self, *, use_nist_data: bool = True) -> None:
-        """Create the element for the Rydberg state."""
-        if hasattr(self, "_element"):
-            raise RuntimeError("The element was already created, you should not create it again.")
-        self._element = BaseElement.from_species(self.species, use_nist_data=use_nist_data)
-
     @property
     @abstractmethod
     def radial(self) -> RadialState: ...
@@ -79,7 +67,7 @@ def get_energy(self, unit: str | None = None) -> PintFloat | float:
         where `\mu = R_M/R_\infty` is the reduced mass and `\nu` the effective principal quantum number.
         """
         nu = self.get_nu()
-        energy_au = self.element.calc_energy_from_nu(nu)
+        energy_au = calc_energy_from_nu(self.species.reduced_mass_au, nu)
         if unit == "a.u.":
             return energy_au
         energy: PintFloat = energy_au * BaseQuantities["ENERGY"]
@@ -199,7 +187,7 @@ class RydbergStateAlkali(RydbergStateBase):
 
     def __init__(
         self,
-        species: str,
+        species: str | SpeciesObject,
         n: int,
         l: int,
         j: float | None = None,
@@ -216,16 +204,17 @@ def __init__(
               Optional, only needed for concrete angular matrix elements.
 
         """
+        if isinstance(species, str):
+            species = SpeciesObject.from_name(species)
         self.species = species
         self.n = n
         self.l = l
         self.j = try_trivial_spin_addition(l, 0.5, j, "j")
         self.m = m
 
-        element = BaseElement.from_species(species)
-        if element.number_valence_electrons != 1:
-            raise ValueError(f"The element {species} is not an alkali atom.")
-        if not element.is_allowed_shell(n, l, s_tot=1 / 2):
+        if species.number_valence_electrons != 1:
+            raise ValueError(f"The species {species.name} is not an alkali atom.")
+        if not species.is_allowed_shell(n, l):
             raise ValueError(f"The shell ({n=}, {l=}) is not allowed for the species {self.species}.")
 
     @cached_property
@@ -242,11 +231,10 @@ def radial(self) -> RadialState:
 
     def __repr__(self) -> str:
         species, n, l, j, m = self.species, self.n, self.l, self.j, self.m
-        return f"{self.__class__.__name__}({species}, {n=}, {l=}, {j=}, {m=})"
+        return f"{self.__class__.__name__}({species.name}, {n=}, {l=}, {j=}, {m=})"
 
     def get_nu(self) -> float:
-        energy_au = self.element.calc_energy(self.n, self.l, self.j, s_tot=1 / 2, unit="a.u.")
-        return self.element.calc_nu_from_energy(energy_au)
+        return self.species.calc_nu(self.n, self.l, self.j, s_tot=1 / 2)
 
 
 class RydbergStateAlkaliHyperfine(RydbergStateBase):
@@ -254,7 +242,7 @@ class RydbergStateAlkaliHyperfine(RydbergStateBase):
 
     def __init__(
         self,
-        species: str,
+        species: str | SpeciesObject,
         n: int,
         l: int,
         j: float | None = None,
@@ -273,18 +261,18 @@ def __init__(
               Optional, only needed for concrete angular matrix elements.
 
         """
-        element = BaseElement.from_species(species)
-
+        if isinstance(species, str):
+            species = SpeciesObject.from_name(species)
         self.species = species
         self.n = n
         self.l = l
         self.j = try_trivial_spin_addition(l, 0.5, j, "j")
-        self.f = try_trivial_spin_addition(self.j, element.i_c, f, "f")
+        self.f = try_trivial_spin_addition(self.j, species.i_c, f, "f")
         self.m = m
 
-        if element.number_valence_electrons != 1:
-            raise ValueError(f"The element {species} is not an alkali atom.")
-        if not element.is_allowed_shell(n, l, s_tot=1 / 2):
+        if species.number_valence_electrons != 1:
+            raise ValueError(f"The species {species.name} is not an alkali atom.")
+        if not species.is_allowed_shell(n, l):
             raise ValueError(f"The shell ({n=}, {l=}) is not allowed for the species {self.species}.")
 
     @cached_property
@@ -301,11 +289,10 @@ def radial(self) -> RadialState:
 
     def __repr__(self) -> str:
         species, n, l, j, f, m = self.species, self.n, self.l, self.j, self.f, self.m
-        return f"{self.__class__.__name__}({species}, {n=}, {l=}, {j=}, {f=}, {m=})"
+        return f"{self.__class__.__name__}({species.name}, {n=}, {l=}, {j=}, {f=}, {m=})"
 
     def get_nu(self) -> float:
-        energy_au = self.element.calc_energy(self.n, self.l, self.j, s_tot=1 / 2, unit="a.u.")
-        return self.element.calc_nu_from_energy(energy_au)
+        return self.species.calc_nu(self.n, self.l, self.j, s_tot=1 / 2)
 
 
 class RydbergStateAlkalineLS(RydbergStateBase):
@@ -313,7 +300,7 @@ class RydbergStateAlkalineLS(RydbergStateBase):
 
     def __init__(
         self,
-        species: str,
+        species: str | SpeciesObject,
         n: int,
         l: int,
         s_tot: float,
@@ -332,6 +319,8 @@ def __init__(
               Optional, only needed for concrete angular matrix elements.
 
         """
+        if isinstance(species, str):
+            species = SpeciesObject.from_name(species)
         self.species = species
         self.n = n
         self.l = l
@@ -339,10 +328,9 @@ def __init__(
         self.j_tot = try_trivial_spin_addition(l, s_tot, j_tot, "j_tot")
         self.m = m
 
-        element = BaseElement.from_species(species)
-        if element.number_valence_electrons != 2:
-            raise ValueError(f"The element {species} is not an alkaline atom.")
-        if not element.is_allowed_shell(n, l, s_tot=s_tot):
+        if species.number_valence_electrons != 2:
+            raise ValueError(f"The species {species.name} is not an alkaline atom.")
+        if not species.is_allowed_shell(n, l, s_tot=s_tot):
             raise ValueError(f"The shell ({n=}, {l=}) is not allowed for the species {self.species}.")
 
     @cached_property
@@ -359,8 +347,7 @@ def radial(self) -> RadialState:
 
     def __repr__(self) -> str:
         species, n, l, s_tot, j_tot, m = self.species, self.n, self.l, self.s_tot, self.j_tot, self.m
-        return f"{self.__class__.__name__}({species}, {n=}, {l=}, {s_tot=}, {j_tot=}, {m=})"
+        return f"{self.__class__.__name__}({species.name}, {n=}, {l=}, {s_tot=}, {j_tot=}, {m=})"
 
     def get_nu(self) -> float:
-        energy_au = self.element.calc_energy(self.n, self.l, self.j_tot, s_tot=self.s_tot, unit="a.u.")
-        return self.element.calc_nu_from_energy(energy_au)
+        return self.species.calc_nu(self.n, self.l, self.j_tot, s_tot=self.s_tot)
diff --git a/src/ryd_numerov/species/__init__.py b/src/ryd_numerov/species/__init__.py
new file mode 100644
index 00000000..bad7e163
--- /dev/null
+++ b/src/ryd_numerov/species/__init__.py
@@ -0,0 +1,25 @@
+from ryd_numerov.species.cesium import Cesium
+from ryd_numerov.species.hydrogen import Hydrogen, HydrogenTextBook
+from ryd_numerov.species.lithium import Lithium
+from ryd_numerov.species.potassium import Potassium
+from ryd_numerov.species.rubidium import Rubidium
+from ryd_numerov.species.sodium import Sodium
+from ryd_numerov.species.species_object import SpeciesObject
+from ryd_numerov.species.strontium import Strontium87, Strontium88
+from ryd_numerov.species.ytterbium import Ytterbium171, Ytterbium173, Ytterbium174
+
+__all__ = [
+    "Cesium",
+    "Hydrogen",
+    "HydrogenTextBook",
+    "Lithium",
+    "Potassium",
+    "Rubidium",
+    "Sodium",
+    "SpeciesObject",
+    "Strontium87",
+    "Strontium88",
+    "Ytterbium171",
+    "Ytterbium173",
+    "Ytterbium174",
+]
diff --git a/src/ryd_numerov/elements/cesium.py b/src/ryd_numerov/species/cesium.py
similarity index 94%
rename from src/ryd_numerov/elements/cesium.py
rename to src/ryd_numerov/species/cesium.py
index 173e7437..77abe87b 100644
--- a/src/ryd_numerov/elements/cesium.py
+++ b/src/ryd_numerov/species/cesium.py
@@ -1,11 +1,11 @@
 from pathlib import Path
 from typing import ClassVar
 
-from ryd_numerov.elements.base_element import BaseElement
+from ryd_numerov.species.species_object import SpeciesObject
 
 
-class Cesium(BaseElement):
-    species = "Cs"
+class Cesium(SpeciesObject):
+    name = "Cs"
     Z = 55
     number_valence_electrons = 1
     ground_state_shell = (6, 0)
diff --git a/src/ryd_numerov/elements/hydrogen.py b/src/ryd_numerov/species/hydrogen.py
similarity index 82%
rename from src/ryd_numerov/elements/hydrogen.py
rename to src/ryd_numerov/species/hydrogen.py
index f0b25cf2..dc28fff0 100644
--- a/src/ryd_numerov/elements/hydrogen.py
+++ b/src/ryd_numerov/species/hydrogen.py
@@ -1,11 +1,11 @@
 from typing import ClassVar
 
-from ryd_numerov.elements.base_element import BaseElement
+from ryd_numerov.species.species_object import SpeciesObject
 from ryd_numerov.units import rydberg_constant
 
 
-class Hydrogen(BaseElement):
-    species = "H"
+class Hydrogen(SpeciesObject):
+    name = "H"
     Z = 1
     number_valence_electrons = 1
     ground_state_shell = (1, 0)
@@ -20,10 +20,10 @@ class Hydrogen(BaseElement):
     _corrected_rydberg_constant = (109677.58340280356, None, "1/cm")
 
 
-class HydrogenTextBook(BaseElement):
+class HydrogenTextBook(SpeciesObject):
     """Hydrogen from QM textbook with infinite nucleus mass and no spin orbit coupling."""
 
-    species = "H_textbook"
+    name = "H_textbook"
     Z = 1
     number_valence_electrons = 1
     ground_state_shell = (1, 0)
diff --git a/src/ryd_numerov/elements/lithium.py b/src/ryd_numerov/species/lithium.py
similarity index 94%
rename from src/ryd_numerov/elements/lithium.py
rename to src/ryd_numerov/species/lithium.py
index 8621b20d..504d4104 100644
--- a/src/ryd_numerov/elements/lithium.py
+++ b/src/ryd_numerov/species/lithium.py
@@ -1,11 +1,11 @@
 from pathlib import Path
 from typing import ClassVar
 
-from ryd_numerov.elements.base_element import BaseElement
+from ryd_numerov.species.species_object import SpeciesObject
 
 
-class Lithium(BaseElement):
-    species = "Li"
+class Lithium(SpeciesObject):
+    name = "Li"
     Z = 3
     number_valence_electrons = 1
     ground_state_shell = (2, 0)
diff --git a/src/ryd_numerov/elements/nist_energy_levels/cesium.txt b/src/ryd_numerov/species/nist_energy_levels/cesium.txt
similarity index 100%
rename from src/ryd_numerov/elements/nist_energy_levels/cesium.txt
rename to src/ryd_numerov/species/nist_energy_levels/cesium.txt
diff --git a/src/ryd_numerov/elements/nist_energy_levels/lithium.txt b/src/ryd_numerov/species/nist_energy_levels/lithium.txt
similarity index 100%
rename from src/ryd_numerov/elements/nist_energy_levels/lithium.txt
rename to src/ryd_numerov/species/nist_energy_levels/lithium.txt
diff --git a/src/ryd_numerov/elements/nist_energy_levels/potassium.txt b/src/ryd_numerov/species/nist_energy_levels/potassium.txt
similarity index 100%
rename from src/ryd_numerov/elements/nist_energy_levels/potassium.txt
rename to src/ryd_numerov/species/nist_energy_levels/potassium.txt
diff --git a/src/ryd_numerov/elements/nist_energy_levels/rubidium.txt b/src/ryd_numerov/species/nist_energy_levels/rubidium.txt
similarity index 100%
rename from src/ryd_numerov/elements/nist_energy_levels/rubidium.txt
rename to src/ryd_numerov/species/nist_energy_levels/rubidium.txt
diff --git a/src/ryd_numerov/elements/nist_energy_levels/sodium.txt b/src/ryd_numerov/species/nist_energy_levels/sodium.txt
similarity index 100%
rename from src/ryd_numerov/elements/nist_energy_levels/sodium.txt
rename to src/ryd_numerov/species/nist_energy_levels/sodium.txt
diff --git a/src/ryd_numerov/elements/nist_energy_levels/strontium.txt b/src/ryd_numerov/species/nist_energy_levels/strontium.txt
similarity index 100%
rename from src/ryd_numerov/elements/nist_energy_levels/strontium.txt
rename to src/ryd_numerov/species/nist_energy_levels/strontium.txt
diff --git a/src/ryd_numerov/elements/nist_energy_levels/ytterbium.txt b/src/ryd_numerov/species/nist_energy_levels/ytterbium.txt
similarity index 100%
rename from src/ryd_numerov/elements/nist_energy_levels/ytterbium.txt
rename to src/ryd_numerov/species/nist_energy_levels/ytterbium.txt
diff --git a/src/ryd_numerov/elements/potassium.py b/src/ryd_numerov/species/potassium.py
similarity index 94%
rename from src/ryd_numerov/elements/potassium.py
rename to src/ryd_numerov/species/potassium.py
index 890eda45..1834fd75 100644
--- a/src/ryd_numerov/elements/potassium.py
+++ b/src/ryd_numerov/species/potassium.py
@@ -1,11 +1,11 @@
 from pathlib import Path
 from typing import ClassVar
 
-from ryd_numerov.elements.base_element import BaseElement
+from ryd_numerov.species.species_object import SpeciesObject
 
 
-class Potassium(BaseElement):
-    species = "K"
+class Potassium(SpeciesObject):
+    name = "K"
     Z = 19
     number_valence_electrons = 1
     ground_state_shell = (4, 0)
diff --git a/src/ryd_numerov/elements/rubidium.py b/src/ryd_numerov/species/rubidium.py
similarity index 94%
rename from src/ryd_numerov/elements/rubidium.py
rename to src/ryd_numerov/species/rubidium.py
index f0838779..5e6e14c4 100644
--- a/src/ryd_numerov/elements/rubidium.py
+++ b/src/ryd_numerov/species/rubidium.py
@@ -1,10 +1,10 @@
 from pathlib import Path
 from typing import ClassVar
 
-from ryd_numerov.elements.base_element import BaseElement
+from ryd_numerov.species.species_object import SpeciesObject
 
 
-class _RubidiumAbstract(BaseElement):
+class _RubidiumAbstract(SpeciesObject):
     Z = 37
     number_valence_electrons = 1
     ground_state_shell = (5, 0)
@@ -54,7 +54,7 @@ class _RubidiumAbstract(BaseElement):
 
 
 class Rubidium87(_RubidiumAbstract):
-    species = "Rb87"
+    name = "Rb87"
     i_c = 3 / 2
 
     _corrected_rydberg_constant = (109736.62301604665, None, "1/cm")
@@ -62,12 +62,12 @@ class Rubidium87(_RubidiumAbstract):
 
 class Rubidium(_RubidiumAbstract):
     # no hyperfine structure, use rydberg constant of Rb87
-    species = "Rb"
+    name = "Rb"
     _corrected_rydberg_constant = (109736.62301604665, None, "1/cm")
 
 
 class Rubidium85(_RubidiumAbstract):
-    species = "Rb85"
+    name = "Rb85"
     i_c = 5 / 2
 
     _corrected_rydberg_constant = (109736.605, None, "1/cm")
diff --git a/src/ryd_numerov/elements/sodium.py b/src/ryd_numerov/species/sodium.py
similarity index 95%
rename from src/ryd_numerov/elements/sodium.py
rename to src/ryd_numerov/species/sodium.py
index d2ca1c86..17cff19f 100644
--- a/src/ryd_numerov/elements/sodium.py
+++ b/src/ryd_numerov/species/sodium.py
@@ -1,11 +1,11 @@
 from pathlib import Path
 from typing import ClassVar
 
-from ryd_numerov.elements.base_element import BaseElement
+from ryd_numerov.species.species_object import SpeciesObject
 
 
-class Sodium(BaseElement):
-    species = "Na"
+class Sodium(SpeciesObject):
+    name = "Na"
     Z = 11
     number_valence_electrons = 1
     ground_state_shell = (3, 0)
diff --git a/src/ryd_numerov/elements/base_element.py b/src/ryd_numerov/species/species_object.py
similarity index 62%
rename from src/ryd_numerov/elements/base_element.py
rename to src/ryd_numerov/species/species_object.py
index 57b3b8eb..e9a6ebda 100644
--- a/src/ryd_numerov/elements/base_element.py
+++ b/src/ryd_numerov/species/species_object.py
@@ -2,7 +2,6 @@
 
 import inspect
 import logging
-import math
 import re
 from abc import ABC
 from fractions import Fraction
@@ -11,6 +10,7 @@
 
 import numpy as np
 
+from ryd_numerov.species.utils import calc_nu_from_energy, convert_electron_configuration
 from ryd_numerov.units import rydberg_constant, ureg
 
 if TYPE_CHECKING:
@@ -22,18 +22,18 @@
 logger = logging.getLogger(__name__)
 
 
-class BaseElement(ABC):
-    """Abstract base class for all elements.
+class SpeciesObject(ABC):
+    """Abstract base class for all species objects.
 
     For the electronic ground state configurations and sorted shells,
     see e.g. https://www.webelements.com/atoms.html
 
     """
 
-    species: ClassVar[str]
-    """Atomic species."""
+    name: ClassVar[str]
+    """The name of the atomic species."""
     Z: ClassVar[int]
-    """Atomic number of the element."""
+    """Atomic number of the species."""
     i_c: ClassVar[float] = 0
     """Nuclear spin, (default 0 to ignore hyperfine structure)."""
     number_valence_electrons: ClassVar[int]
@@ -48,7 +48,7 @@ class BaseElement(ABC):
     _ionization_energy: tuple[float, float | None, str]
     """Ionization energy with uncertainty and unit: (value, uncertainty, unit)."""
 
-    # Parameters for the extended Rydberg Ritz formula, see calc_energy
+    # Parameters for the extended Rydberg Ritz formula, see calc_nu
     _quantum_defects: ClassVar[dict[tuple[int, float, float], tuple[float, float, float, float, float]] | None] = None
     """Dictionary containing the quantum defects for each (l, j_tot, s_tot) combination, i.e.
     _quantum_defects[(l,j_tot,s_tot)] = (d0, d2, d4, d6, d8)
@@ -58,7 +58,7 @@ class BaseElement(ABC):
     r"""Corrected Rydberg constant stored as (value, uncertainty, unit)"""
 
     potential_type_default: PotentialType | None = None
-    """Default potential type to use for this element. If None, the potential type must be specified explicitly.
+    """Default potential type to use for this species. If None, the potential type must be specified explicitly.
     In general, it looks like marinescu_1993 is better for alkali atoms, and fei_2009 is better for alkaline earth atoms
     """
 
@@ -83,26 +83,20 @@ class BaseElement(ABC):
     """
 
     _nist_energy_levels_file: Path | None = None
-    """Path to the NIST energy levels file for this element.
+    """Path to the NIST energy levels file for this species.
     The file should be directly downloaded from https://physics.nist.gov/PhysRefData/ASD/levels_form.html
     in the 'Tab-delimited' format and in units of Hartree.
     """
 
-    def __init__(self, use_nist_data: bool = True, *, nist_n_max: int = 15) -> None:
-        """Initialize an element instance.
+    def __init__(self) -> None:
+        """Initialize an species instance.
 
-        Use this init method to set up additional properties and data for the element,
+        Use this init method to set up additional properties and data for the species,
         like loading NIST energy levels from a file.
 
-        Args:
-            use_nist_data: Whether to use NIST data for this element. Default is True.
-            nist_n_max: Maximum principal quantum number for which to load the NIST energy levels. Default is 15.
-
         """
         self._nist_energy_levels: dict[tuple[int, int, float, float], float] = {}
-        self._nist_n_max = nist_n_max
-        self.use_nist_data = use_nist_data
-        if use_nist_data and self._nist_energy_levels_file is not None:
+        if self._nist_energy_levels_file is not None:
             self._setup_nist_energy_levels(self._nist_energy_levels_file)
 
     def _setup_nist_energy_levels(self, file: Path) -> None:  # noqa: C901, PLR0912
@@ -170,57 +164,59 @@ def _setup_nist_energy_levels(self, file: Path) -> None:  # noqa: C901, PLR0912
                 self._nist_energy_levels[(n, l, j_tot, s_tot)] = energy
 
         if len(self._nist_energy_levels) == 0:
-            raise ValueError(f"No NIST energy levels found for element {self.species} in file {file}.")
+            raise ValueError(f"No NIST energy levels found for species {self.name} in file {file}.")
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}()"
 
     @classmethod
     @cache
-    def from_species(cls, species: str, use_nist_data: bool = True) -> BaseElement:
-        """Create an instance of the element class from the species string.
+    def from_name(cls, name: str) -> SpeciesObject:
+        """Create an instance of the species class from the species name.
 
-        This method searches through all subclasses of BaseElement until it finds one with a matching species attribute.
-        This approach allows for easy extension of the library with new elements.
-        A user can even subclass BaseElement in his code (without modifying the ryd-numerov library),
-        e.g. `class CustomRubidium(BaseElement): species = "Custom_Rb" ...`
-        and then use the new element by calling RydbergStateAlkali("Custom_Rb", ...)
+        This method searches through all subclasses of SpeciesObject until it finds one with a matching species name.
+        This approach allows for easy extension of the library with new species.
+        A user can even subclass SpeciesObject in his code (without modifying the ryd-numerov library),
+        e.g. `class CustomRubidium(SpeciesObject): name = "Custom_Rb" ...`
+        and then use the new species by calling RydbergStateAlkali("Custom_Rb", ...)
 
         Args:
-            species: The species string (e.g. "Rb").
-            use_nist_data: Whether to use NIST data for this element. Default is True.
+            name: The species name (e.g. "Rb").
 
         Returns:
-            An instance of the corresponding element class.
+            An instance of the corresponding species class.
 
         """
         concrete_subclasses = cls._get_concrete_subclasses()
         for subclass in concrete_subclasses:
-            if subclass.species == species:
-                return subclass(use_nist_data=use_nist_data)
+            if subclass.name == name:
+                return subclass()
         raise ValueError(
-            f"Unknown species: {species}. Available species: {[subclass.species for subclass in concrete_subclasses]}"
+            f"Unknown species name: {name}. Available species: {[subclass.name for subclass in concrete_subclasses]}"
         )
 
     @classmethod
-    def _get_concrete_subclasses(cls) -> list[type[BaseElement]]:
+    def _get_concrete_subclasses(cls) -> list[type[SpeciesObject]]:
         subclasses = []
         for subclass in cls.__subclasses__():
-            if not inspect.isabstract(subclass) and hasattr(subclass, "species"):
+            if not inspect.isabstract(subclass) and hasattr(subclass, "name"):
                 subclasses.append(subclass)
             subclasses.extend(subclass._get_concrete_subclasses())  # noqa: SLF001
         return subclasses
 
     @classmethod
     def get_available_species(cls) -> list[str]:
-        """Get a list of all available species in the library.
+        """Get a list of all available species names in the library.
 
-        This method returns a list of species strings for all concrete subclasses of BaseElement.
+        This method returns a list of species names for all concrete subclasses of SpeciesObject.
 
         Returns:
-            List of species strings.
+            List of species names.
 
         """
-        return sorted([subclass.species for subclass in cls._get_concrete_subclasses()])
+        return sorted([subclass.name for subclass in cls._get_concrete_subclasses()])
 
-    def is_allowed_shell(self, n: int, l: int, s_tot: float) -> bool:
+    def is_allowed_shell(self, n: int, l: int, s_tot: float | None = None) -> bool:
         """Check if the quantum numbers describe an allowed shell.
 
         I.e. whether the shell is above the ground state shell.
@@ -234,14 +230,17 @@ def is_allowed_shell(self, n: int, l: int, s_tot: float) -> bool:
             True if the quantum numbers specify a shell equal to or above the ground state shell, False otherwise.
 
         """
-        assert (self.number_valence_electrons == 1 and s_tot == 1 / 2) or (
-            self.number_valence_electrons == 2 and s_tot in (0, 1)
-        ), f"Invalid spin {s_tot=} for {self.species}."
-
-        if self.number_valence_electrons == 2 and s_tot == 1 and (n, l) == self.ground_state_shell:
-            return False  # For alkaline earth atoms, the triplet state of the ground state shell is not allowed
+        if s_tot is None:
+            if self.number_valence_electrons > 1:
+                raise ValueError("s_tot must be specified for species with more than one valence electron.")
+            s_tot = self.number_valence_electrons / 2
+        if (self.number_valence_electrons / 2) % 1 != s_tot % 1 or s_tot > self.number_valence_electrons / 2:
+            raise ValueError(f"Invalid spin {s_tot=} for {self.name}.")
+
+        if (n, l) == self.ground_state_shell:
+            return s_tot != 1  # For alkaline earth atoms, the triplet state of the ground state shell is not allowed
         if n < 1 or l < 0 or l >= n:
-            raise ValueError(f"Invalid shell: (n={n}, l={l}). Must be n >= 1 and 0 <= l < n.")
+            raise ValueError(f"Invalid shell: (n={n}, l={l}). Must be n >= 1 and 0 <= l <= n-1.")
         if (n, l) >= self.ground_state_shell:
             return True
         return (n, l) in self._additional_allowed_shells
@@ -282,7 +281,7 @@ def get_corrected_rydberg_constant(self, unit: str | None = "hartree") -> PintFl
         The corrected Rydberg constant is defined as
 
         .. math::
-            R_M = R_\infty * \frac{m_{Core}}{m_{Core} + m_e}
+            R_M = R_\infty \frac{m_{Core}}{m_{Core} + m_e}
 
         where :math:`R_\infty` is the Rydberg constant for infinite nuclear mass,
         :math:`m_{Core}` is the mass of the core,
@@ -306,138 +305,83 @@ def get_corrected_rydberg_constant(self, unit: str | None = "hartree") -> PintFl
         return corrected_rydberg_constant.to(unit, "spectroscopy").magnitude
 
     @cached_property  # don't remove this caching without benchmarking it!!!
-    def reduced_mass_factor(self) -> float:
-        r"""The reduced mass factor \mu.
+    def reduced_mass_au(self) -> float:
+        r"""The reduced mass mu in atomic units.
 
-        The reduced mass factor
+        The reduced mass in atomic units :math:`\mu / m_e` is given by
 
         .. math::
-            \mu = \frac{m_{Core}}{m_{Core} + m_e}
+            \frac{\mu}{m_e} = \frac{m_{Core}}{m_{Core} + m_e}
 
-        calculated via the corrected Rydberg constant
+        We calculate the reduced mass via the corrected Rydberg constant
 
         .. math::
-            \mu = \frac{R_M}{R_\infty}
+            \frac{\mu}{m_e} = \frac{R_M}{R_\infty}
 
         """
         return self.get_corrected_rydberg_constant("hartree") / rydberg_constant.to("hartree").m
 
-    @overload
-    def calc_energy(
-        self, n: int, l: int, j_tot: float, s_tot: float | None = None, *, unit: None = None
-    ) -> PintFloat: ...
-
-    @overload
-    def calc_energy(self, n: int, l: int, j_tot: float, s_tot: float | None = None, *, unit: str) -> float: ...
-
-    def calc_energy(  # noqa: C901
-        self, n: int, l: int, j_tot: float, s_tot: float | None = None, *, unit: str | None = "hartree"
-    ) -> PintFloat | float:
-        r"""Calculate the energy of a Rydberg state with for the given n, l, j_tot and s_tot.
-
-        I.e. either look up the energy for low lying states in the nist data,
-        or calculate it via the quantum defect theory.
-
-        The effective principal quantum number in quantum defect theory
-        is defined as series expansion :math:`n^* = n - \delta_{lj}(n)`
-        where
+    def calc_nu(
+        self,
+        n: int,
+        l: int,
+        j_tot: float,
+        s_tot: float | None = None,
+        *,
+        use_nist_data: bool = True,
+        nist_n_max: int = 15,
+    ) -> float:
+        r"""Calculate the effective principal quantum number nu of a Rydberg state with the given n, l, j_tot and s_tot.
+
+        I.e. either look up the energy for low lying states in the nist data (if use_nist_data is True),
+        and calculate nu from the energy via (see also `calc_nu_from_energy`):
 
         .. math::
-            \delta_{lj}(n) = d0_{lj} + d2_{lj} / [n - d0_{lj}(n)]^2 + d4_{lj} / [n - \delta_{lj}(n)]^4 + ...
+            \nu = \sqrt{\frac{1}{2} \frac{\mu/m_e}{-E/E_H}}
 
-
-        is the quantum defect. The energy of the Rydberg state is then given by
+        Or calculate nu via the quantum defect theory,
+        where nu is defined as series expansion :math:`\nu = n^* = n - \delta_{lj}(n)`
+        with the quantum defect
 
         .. math::
-            E_{nlj} / E_H = -\frac{1}{2} \frac{Ry}{Ry_\infty} \frac{1}{n^*}
-
-        where :math:`E_H` is the Hartree energy (the atomic unit of energy).
+            \delta_{lj}(n) = d0_{lj} + d2_{lj} / [n - d0_{lj}(n)]^2 + d4_{lj} / [n - \delta_{lj}(n)]^4 + ...
 
         References:
             - On a New Law of Series Spectra, Ritz; DOI: 10.1086/141591, https://ui.adsabs.harvard.edu/abs/1908ApJ....28..237R/abstract
             - Rydberg atoms, Gallagher; DOI: 10.1088/0034-4885/51/2/001, (Eq. 16.19)
 
+        Args:
+            n: The principal quantum number of the Rydberg state.
+            l: The orbital angular momentum quantum number of the Rydberg state.
+            j_tot: The total angular momentum quantum number of the Rydberg state.
+            s_tot: The total spin quantum number of the Rydberg state.
+            use_nist_data: Whether to use NIST energy data.
+                Default is True.
+            nist_n_max: Maximum principal quantum number for which to use the NIST energy data.
+                Default is 15.
+
         """
         if s_tot is None:
             if self.number_valence_electrons != 1:
-                raise ValueError("s_tot must be specified for elements with more than one valence electron.")
+                raise ValueError("s_tot must be specified for species with more than one valence electron.")
             s_tot = 0.5
         if (s_tot % 1) != ((self.number_valence_electrons / 2) % 1):
-            raise ValueError(f"Invalid spin {s_tot=} for {self.species}.")
+            raise ValueError(f"Invalid spin {s_tot=} for {self.name}.")
         if j_tot % 1 != (l + s_tot) % 1:
             raise ValueError(f"Invalid quantum numbers: ({l=}, {j_tot=}, {s_tot=})")
 
-        energy_au: float | None = None
-        if n <= self._nist_n_max and self.use_nist_data:
+        if n <= nist_n_max and use_nist_data:  # try to use NIST data
             if (n, l, j_tot, s_tot) in self._nist_energy_levels:
                 energy_au = self._nist_energy_levels[(n, l, j_tot, s_tot)]
                 energy_au -= self.get_ionization_energy("hartree")
-            else:
-                logger.debug(
-                    "NIST energy levels for (n=%d, l=%d, j_tot=%s, s_tot=%s) not found, using quantum defect theory.",
-                    *(n, l, j_tot, s_tot),
-                )
-
-        if energy_au is None:
-            if self._quantum_defects is None:
-                raise ValueError(f"No quantum defect data available for element {self.species}.")
-            d0, d2, d4, d6, d8 = self._quantum_defects.get((l, j_tot, s_tot), (0, 0, 0, 0, 0))
-            delta_nlj = d0 + d2 / (n - d0) ** 2 + d4 / (n - d0) ** 4 + d6 / (n - d0) ** 6 + d8 / (n - d0) ** 8
-            n_star = n - delta_nlj
-            energy_au = self.calc_energy_from_nu(n_star)
-
-        energy: PintFloat = ureg.Quantity(energy_au, "hartree")
-        if unit is None:
-            return energy
-        if unit == "a.u.":
-            return energy.magnitude
-        return energy.to(unit, "spectroscopy").magnitude
-
-    def calc_nu_from_energy(self, energy_au: float) -> float:
-        r"""Calculate the effective principal quantum number nu from a given energy.
-
-        The effective principal quantum number is given by
-
-        .. math::
-            \nu = \sqrt{\frac{1}{2} \frac{\mu}{-E}}
-
-        where :math:`\mu` is the reduced mass factor and :math:`E` the energy in atomic units.
-
-        """
-        nu = math.sqrt(0.5 * self.reduced_mass_factor / -energy_au)
-        if abs(nu - round(nu)) < 1e-10:
-            nu = round(nu)
-        return nu
-
-    def calc_energy_from_nu(self, nu: float) -> float:
-        r"""Calculate the energy from a given effective principal quantum number nu.
-
-        The energy is given by
-
-        .. math::
-            E = -\frac{1}{2} \frac{\mu}{\nu^2}
-
-        where :math:`\mu` is the reduced mass factor and :math:`\nu` the effective principal quantum number.
-        """
-        return -0.5 * self.reduced_mass_factor / nu**2
-
-
-def convert_electron_configuration(config: str) -> list[tuple[int, int, int]]:
-    """Convert an electron configuration string to a list of tuples [(n, l, number), ...].
+                return calc_nu_from_energy(self.reduced_mass_au, energy_au)
+            logger.debug(
+                "NIST energy levels for (n=%d, l=%d, j_tot=%s, s_tot=%s) not found, using quantum defect theory.",
+                *(n, l, j_tot, s_tot),
+            )
 
-    This means convert a string representing the outermost electrons
-    like "4f14.6s" to [(4, 2, 14), (6, 0, 1)].
-    """
-    l_str2int = {"s": 0, "p": 1, "d": 2, "f": 3, "g": 4, "h": 5, "i": 6, "k": 7, "l": 8, "m": 9}
-    parts = config.split(".")
-    converted_parts = []
-    for part in parts:
-        match = re.match(r"^(\d+)([a-z])(\d*)$", part)
-        if match is None:
-            raise ValueError(f"Invalid configuration format: {config}.")
-        n = int(match.group(1))
-        l = l_str2int[match.group(2)]
-        number = int(match.group(3)) if match.group(3) else 1
-        converted_parts.append((n, l, number))
-
-    return converted_parts
+        if self._quantum_defects is None:
+            raise ValueError(f"No quantum defect data available for species {self.name}.")
+        d0, d2, d4, d6, d8 = self._quantum_defects.get((l, j_tot, s_tot), (0, 0, 0, 0, 0))
+        delta_nlj = d0 + d2 / (n - d0) ** 2 + d4 / (n - d0) ** 4 + d6 / (n - d0) ** 6 + d8 / (n - d0) ** 8
+        return n - delta_nlj
diff --git a/src/ryd_numerov/elements/strontium.py b/src/ryd_numerov/species/strontium.py
similarity index 95%
rename from src/ryd_numerov/elements/strontium.py
rename to src/ryd_numerov/species/strontium.py
index 9b15134a..8242b6ec 100644
--- a/src/ryd_numerov/elements/strontium.py
+++ b/src/ryd_numerov/species/strontium.py
@@ -3,11 +3,11 @@
 from pathlib import Path
 from typing import ClassVar
 
-from ryd_numerov.elements.base_element import BaseElement
+from ryd_numerov.species.species_object import SpeciesObject
 from ryd_numerov.units import electron_mass, rydberg_constant
 
 
-class _StrontiumAbstract(BaseElement):
+class _StrontiumAbstract(SpeciesObject):
     Z = 38
     number_valence_electrons = 2
     ground_state_shell = (5, 0)
@@ -36,7 +36,7 @@ class _StrontiumAbstract(BaseElement):
 
 
 class Strontium88(_StrontiumAbstract):
-    species = "Sr88"
+    name = "Sr88"
     i_c = 0
 
     # https://physics.nist.gov/PhysRefData/Handbook/Tables/strontiumtable1.htm
@@ -73,7 +73,7 @@ class Strontium88(_StrontiumAbstract):
 
 
 class Strontium87(_StrontiumAbstract):
-    species = "Sr87"
+    name = "Sr87"
     i_c = 9 / 2
 
     # https://physics.nist.gov/PhysRefData/Handbook/Tables/strontiumtable1.htm
diff --git a/src/ryd_numerov/species/utils.py b/src/ryd_numerov/species/utils.py
new file mode 100644
index 00000000..f92fe988
--- /dev/null
+++ b/src/ryd_numerov/species/utils.py
@@ -0,0 +1,72 @@
+import math
+import re
+
+
+def calc_nu_from_energy(reduced_mass_au: float, energy_au: float) -> float:
+    r"""Calculate the effective principal quantum number nu from a given energy.
+
+    The effective principal quantum number is given by
+
+    .. math::
+        \nu
+        = \sqrt{\frac{1}{2} \frac{R_M/R_\infty}{-E/E_H}}
+        = \sqrt{\frac{1}{2} \frac{\mu/m_e}{-E/E_H}}
+
+    where :math:`\mu/m_e` is the reduced mass in atomic units and :math:`E/E_H` the energy in atomic units.
+
+    Args:
+        reduced_mass_au: The reduced mass in atomic units (electron mass).
+        energy_au: The energy in atomic units (hartree).
+
+    Returns:
+        The effective principal quantum number nu.
+
+    """
+    nu = math.sqrt(0.5 * reduced_mass_au / -energy_au)
+    if abs(nu - round(nu)) < 1e-10:
+        nu = round(nu)
+    return nu
+
+
+def calc_energy_from_nu(reduced_mass_au: float, nu: float) -> float:
+    r"""Calculate the energy from a given effective principal quantum number nu.
+
+    The energy is given by
+
+    .. math::
+        E/E_H
+        = -\frac{1}{2} \frac{R_M/R_\infty}{\nu^2}
+        = -\frac{1}{2} \frac{\mu/m_e}{\nu^2}
+
+    where :math:`\mu/m_e` is the reduced mass in atomic units and :math:`\nu` the effective principal quantum number.
+
+    Args:
+        reduced_mass_au: The reduced mass in atomic units :math:`\mu/m_e = \frac{m_{Core}}{m_{Core} + m_e}`.
+        nu: The effective principal quantum number :math:`\nu`.
+
+    Returns:
+        The energy E in atomic units (hartree).
+
+    """
+    return -0.5 * reduced_mass_au / nu**2
+
+
+def convert_electron_configuration(config: str) -> list[tuple[int, int, int]]:
+    """Convert an electron configuration string to a list of tuples [(n, l, number), ...].
+
+    This means convert a string representing the outermost electrons
+    like "4f14.6s" to [(4, 2, 14), (6, 0, 1)].
+    """
+    l_str2int = {"s": 0, "p": 1, "d": 2, "f": 3, "g": 4, "h": 5, "i": 6, "k": 7, "l": 8, "m": 9}
+    parts = config.split(".")
+    converted_parts = []
+    for part in parts:
+        match = re.match(r"^(\d+)([a-z])(\d*)$", part)
+        if match is None:
+            raise ValueError(f"Invalid configuration format: {config}.")
+        n = int(match.group(1))
+        l = l_str2int[match.group(2)]
+        number = int(match.group(3)) if match.group(3) else 1
+        converted_parts.append((n, l, number))
+
+    return converted_parts
diff --git a/src/ryd_numerov/elements/ytterbium.py b/src/ryd_numerov/species/ytterbium.py
similarity index 91%
rename from src/ryd_numerov/elements/ytterbium.py
rename to src/ryd_numerov/species/ytterbium.py
index b03e1d6b..987d02fc 100644
--- a/src/ryd_numerov/elements/ytterbium.py
+++ b/src/ryd_numerov/species/ytterbium.py
@@ -1,11 +1,11 @@
 from pathlib import Path
 from typing import ClassVar
 
-from ryd_numerov.elements.base_element import BaseElement
+from ryd_numerov.species.species_object import SpeciesObject
 from ryd_numerov.units import electron_mass, rydberg_constant
 
 
-class _YtterbiumAbstract(BaseElement):
+class _YtterbiumAbstract(SpeciesObject):
     Z = 70
     number_valence_electrons = 2
     ground_state_shell = (6, 0)
@@ -24,7 +24,7 @@ class _YtterbiumAbstract(BaseElement):
 
 
 class Ytterbium171(_YtterbiumAbstract):
-    species = "Yb171"
+    name = "Yb171"
     i_c = 1 / 2
 
     # https://physics.nist.gov/PhysRefData/Handbook/Tables/ytterbiumtable1.htm
@@ -37,7 +37,7 @@ class Ytterbium171(_YtterbiumAbstract):
 
 
 class Ytterbium173(_YtterbiumAbstract):
-    species = "Yb173"
+    name = "Yb173"
     i_c = 5 / 2
 
     # https://physics.nist.gov/PhysRefData/Handbook/Tables/ytterbiumtable1.htm
@@ -50,7 +50,7 @@ class Ytterbium173(_YtterbiumAbstract):
 
 
 class Ytterbium174(_YtterbiumAbstract):
-    species = "Yb174"
+    name = "Yb174"
     i_c = 0
 
     # https://physics.nist.gov/PhysRefData/Handbook/Tables/ytterbiumtable1.htm
diff --git a/tests/test_all_elements.py b/tests/test_all_elements.py
index 3cba6b1b..05472b0d 100644
--- a/tests/test_all_elements.py
+++ b/tests/test_all_elements.py
@@ -1,25 +1,25 @@
 from typing import TYPE_CHECKING
 
 import pytest
-from ryd_numerov.elements import BaseElement
 from ryd_numerov.rydberg_state import RydbergStateAlkali, RydbergStateAlkalineLS
+from ryd_numerov.species import SpeciesObject
 
 if TYPE_CHECKING:
     from ryd_numerov.rydberg_state import RydbergStateBase
 
 
-@pytest.mark.parametrize("species", BaseElement.get_available_species())
-def test_magnetic(species: str) -> None:
+@pytest.mark.parametrize("species_name", SpeciesObject.get_available_species())
+def test_magnetic(species_name: str) -> None:
     """Test magnetic units."""
-    element = BaseElement.from_species(species)
+    species = SpeciesObject.from_name(species_name)
 
     state: RydbergStateBase
-    if element.number_valence_electrons == 1:
+    if species.number_valence_electrons == 1:
         state = RydbergStateAlkali(species, n=50, l=0)
         state.radial.create_wavefunction()
         with pytest.raises(ValueError, match="j must be set"):
             RydbergStateAlkali(species, n=50, l=1)
-    elif element.number_valence_electrons == 2 and element._quantum_defects is not None:  # noqa: SLF001
+    elif species.number_valence_electrons == 2 and species._quantum_defects is not None:  # noqa: SLF001
         for s_tot in [0, 1]:
             state = RydbergStateAlkalineLS(species, n=50, l=1, s_tot=s_tot, j_tot=1 + s_tot)
             state.radial.create_wavefunction()
diff --git a/tests/test_radial_matrix_element.py b/tests/test_radial_matrix_element.py
index d01ee04f..372cc0f0 100644
--- a/tests/test_radial_matrix_element.py
+++ b/tests/test_radial_matrix_element.py
@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
-from ryd_numerov.elements import BaseElement
 from ryd_numerov.radial import RadialState
 from ryd_numerov.rydberg_state import RydbergStateAlkali
+from ryd_numerov.species import SpeciesObject
 
 
 @pytest.mark.parametrize(
@@ -36,7 +36,7 @@ def test_circular_matrix_element(species: str, n: int, dn: int, dl: int) -> None
 
 
 @pytest.mark.parametrize(
-    ("species", "n", "l", "j_tot"),
+    ("species_name", "n", "l", "j_tot"),
     [
         # for hydrogen the expectation value of r is exact for all states
         ("H", 1, 0, 0.5),
@@ -49,7 +49,7 @@ def test_circular_matrix_element(species: str, n: int, dn: int, dl: int) -> None
         ("Rb", 88, 87, 86.5),
     ],
 )
-def test_circular_expectation_value(species: str, n: int, l: int, j_tot: float) -> None:
+def test_circular_expectation_value(species_name: str, n: int, l: int, j_tot: float) -> None:
     """For circular states, the expectation value of r should be the same as for the hydrogen atom.
 
     For hydrogen the expectation values of r and r^2 are given by
@@ -58,9 +58,8 @@ def test_circular_expectation_value(species: str, n: int, l: int, j_tot: float)
         <r>_{nl} = 1/2 (3 n^2 - l(l+1))
         <r^2>_{nl} = n^2/2 (5 n^2 - 3 l(l+1) + 1)
     """
-    element = BaseElement.from_species(species)
-    energy_au = element.calc_energy(n, l, j_tot, unit="hartree")
-    nu = element.calc_nu_from_energy(energy_au)
+    species = SpeciesObject.from_name(species_name)
+    nu = species.calc_nu(n, l, j_tot)
 
     state = RadialState(species, nu=nu, l_r=l)
     state.set_n_for_sanity_check(n)