morse potential

docs/reference/beignet.datasets.md

@@ -39,7 +39,6 @@
 ::: beignet.datasets.MartinsBloodBrainBarrierDataset
 ::: beignet.datasets.NCATSPAMPAPermeabilityDataset
 ::: beignet.datasets.ObachHalfLifeDataset
-::: beignet.datasets.PDB70Dataset
 ::: beignet.datasets.PDBTrajectoryDataset
 ::: beignet.datasets.PDBbindDataset
 ::: beignet.datasets.ParquetDataset

docs/reference/datasets/sequences.md

@@ -8,7 +8,6 @@
 ::: beignet.datasets.FreeSolvDataset
 ::: beignet.datasets.GDSC1Dataset
 ::: beignet.datasets.GDSC2Dataset
-::: beignet.datasets.PDB70Dataset
 ::: beignet.datasets.PDBbindDataset
 ::: beignet.datasets.REALDatabaseDataset
 ::: beignet.datasets.SKEMPIDataset

docs/reference/operators/interatomic-potentials.md

@@ -1 +1,2 @@
 ::: beignet.lennard_jones_potential
+::: beignet.morse_potential

mkdocs.yml

@@ -34,7 +34,6 @@ nav:
               - "Probabilists’ Hermite polynomial": "reference/operators/special-functions/orthogonal-polynomials/probabilists-hermite-polynomial.md"
               - "Laguerre polynomial": "reference/operators/special-functions/orthogonal-polynomials/laguerre-polynomial.md"
               - "Legendre polynomial": "reference/operators/special-functions/orthogonal-polynomials/legendre-polynomial.md"
-      - "Thermodynamics": "reference/operators/thermodynamics.md"
     - "beignet.datasets": "reference/beignet.datasets.md"
     - "beignet.features":
         - "General-purpose": "reference/features/general-purpose.md"

src/beignet/__init__.py

@@ -194,6 +194,7 @@
 from ._linear_probabilists_hermite_polynomial import (
     linear_probabilists_hermite_polynomial,
 )
+from ._morse_potential import morse_potential
 from ._multiply_chebyshev_polynomial import multiply_chebyshev_polynomial
 from ._multiply_chebyshev_polynomial_by_x import multiply_chebyshev_polynomial_by_x
 from ._multiply_laguerre_polynomial import multiply_laguerre_polynomial
@@ -370,14 +371,9 @@
     "apply_rotation_matrix",
     "apply_rotation_vector",
     "apply_transform",
-    "evaluate_chebyshev_polynomial_cartesian_2d",
-    "evaluate_chebyshev_polynomial_cartesian_3d",
-    "chebyshev_interpolation",
-    "linear_chebyshev_polynomial",
-    "multiply_chebyshev_polynomial_by_x",
-    "chebyshev_zeros",
     "chebyshev_extrema",
     "chebyshev_gauss_quadrature",
+    "chebyshev_interpolation",
     "chebyshev_polynomial_companion",
     "chebyshev_polynomial_domain",
     "chebyshev_polynomial_from_roots",
@@ -391,6 +387,7 @@
     "chebyshev_polynomial_weight",
     "chebyshev_polynomial_x",
     "chebyshev_polynomial_zero",
+    "chebyshev_zeros",
     "compose_euler_angle",
     "compose_quaternion",
     "compose_rotation_matrix",
@@ -416,22 +413,34 @@
     "evaluate_chebyshev_polynomial",
     "evaluate_chebyshev_polynomial_2d",
     "evaluate_chebyshev_polynomial_3d",
+    "evaluate_chebyshev_polynomial_cartesian_2d",
+    "evaluate_chebyshev_polynomial_cartesian_3d",
     "evaluate_laguerre_polynomial",
     "evaluate_laguerre_polynomial_2d",
     "evaluate_laguerre_polynomial_3d",
+    "evaluate_laguerre_polynomial_cartesian_2d",
+    "evaluate_laguerre_polynomial_cartesian_3d",
     "evaluate_legendre_polynomial",
     "evaluate_legendre_polynomial_2d",
     "evaluate_legendre_polynomial_3d",
+    "evaluate_legendre_polynomial_cartesian_2d",
+    "evaluate_legendre_polynomial_cartesian_3d",
     "evaluate_physicists_hermite_polynomial",
     "evaluate_physicists_hermite_polynomial_2d",
     "evaluate_physicists_hermite_polynomial_3d",
+    "evaluate_physicists_hermite_polynomial_cartesian_2d",
+    "evaluate_physicists_hermite_polynomial_cartesian_3d",
     "evaluate_polynomial",
     "evaluate_polynomial_2d",
     "evaluate_polynomial_3d",
+    "evaluate_polynomial_cartesian_2d",
+    "evaluate_polynomial_cartesian_3d",
     "evaluate_polynomial_from_roots",
     "evaluate_probabilists_hermite_polynomial",
     "evaluate_probabilists_hermite_polynomial_2d",
     "evaluate_probabilists_hermite_polynomial_3d",
+    "evaluate_probabilists_hermite_polynomial_cartersian_2d",
+    "evaluate_probabilists_hermite_polynomial_cartersian_3d",
     "farthest_first_traversal",
     "fit_chebyshev_polynomial",
     "fit_laguerre_polynomial",
@@ -443,14 +452,6 @@
     "gauss_legendre_quadrature",
     "gauss_physicists_hermite_polynomial_quadrature",
     "gauss_probabilists_hermite_polynomial_quadrature",
-    "evaluate_probabilists_hermite_polynomial_cartersian_2d",
-    "evaluate_probabilists_hermite_polynomial_cartersian_3d",
-    "linear_probabilists_hermite_polynomial",
-    "multiply_probabilists_hermite_polynomial_by_x",
-    "evaluate_physicists_hermite_polynomial_cartesian_2d",
-    "evaluate_physicists_hermite_polynomial_cartesian_3d",
-    "linear_physicists_hermite_polynomial",
-    "multiply_physicists_hermite_polynomial_by_x",
     "integrate_chebyshev_polynomial",
     "integrate_laguerre_polynomial",
     "integrate_legendre_polynomial",
@@ -462,10 +463,6 @@
     "invert_rotation_matrix",
     "invert_rotation_vector",
     "invert_transform",
-    "evaluate_laguerre_polynomial_cartesian_2d",
-    "evaluate_laguerre_polynomial_cartesian_3d",
-    "linear_laguerre_polynomial",
-    "multiply_laguerre_polynomial_by_x",
     "laguerre_polynomial_companion",
     "laguerre_polynomial_domain",
     "laguerre_polynomial_from_roots",
@@ -492,17 +489,26 @@
     "legendre_polynomial_weight",
     "legendre_polynomial_x",
     "legendre_polynomial_zero",
-    "evaluate_legendre_polynomial_cartesian_2d",
-    "evaluate_legendre_polynomial_cartesian_3d",
-    "linear_legendre_polynomial",
-    "multiply_legendre_polynomial_by_x",
     "lennard_jones_potential",
+    "linear_chebyshev_polynomial",
+    "linear_laguerre_polynomial",
+    "linear_legendre_polynomial",
+    "linear_physicists_hermite_polynomial",
+    "linear_polynomial",
+    "linear_probabilists_hermite_polynomial",
+    "morse_potential",
     "multiply_chebyshev_polynomial",
+    "multiply_chebyshev_polynomial_by_x",
     "multiply_laguerre_polynomial",
+    "multiply_laguerre_polynomial_by_x",
     "multiply_legendre_polynomial",
+    "multiply_legendre_polynomial_by_x",
     "multiply_physicists_hermite_polynomial",
+    "multiply_physicists_hermite_polynomial_by_x",
     "multiply_polynomial",
+    "multiply_polynomial_by_x",
     "multiply_probabilists_hermite_polynomial",
+    "multiply_probabilists_hermite_polynomial_by_x",
     "physicists_hermite_polynomial_companion",
     "physicists_hermite_polynomial_domain",
     "physicists_hermite_polynomial_from_roots",
@@ -516,10 +522,6 @@
     "physicists_hermite_polynomial_weight",
     "physicists_hermite_polynomial_x",
     "physicists_hermite_polynomial_zero",
-    "evaluate_polynomial_cartesian_2d",
-    "evaluate_polynomial_cartesian_3d",
-    "linear_polynomial",
-    "multiply_polynomial_by_x",
     "polynomial_companion",
     "polynomial_domain",
     "polynomial_from_roots",

src/beignet/_morse_potential.py

@@ -0,0 +1,46 @@
+import torch
+from torch import Tensor
+
+
+def morse_potential(
+    input: Tensor,
+    sigma: Tensor | None = 1.0,
+    epsilon: Tensor | None = 5.0,
+    alpha: Tensor | None = 5.0,
+    **_,
+) -> Tensor:
+    """
+    Morse interaction between particles with a minimum at `sigma`.
+
+    Parameters
+    ----------
+    input : Tensor, shape=[n, m]
+        Pairwise distances between particles.
+
+    sigma : Tensor, optional
+        Distance between particles where the energy has a minimum. Should
+        either be a floating-point scalar or a Tensor of shape `[n, m]`.
+
+    epsilon : Tensor, optional
+        Interaction energy scale. Should either be a floating-point scalar or
+        a Tensor of shape `[n, m]`.
+
+    alpha : Tensor, optional
+        Range parameter. Should either be a floating-point scalar or a Tensor
+        of shape `[n, m]`.
+
+    Returns
+    -------
+    output : Tensor, shape=[n, m]
+        Energies.
+    """
+    if sigma is None:
+        sigma = torch.tensor(1.0, dtype=input.dtype)
+
+    if epsilon is None:
+        epsilon = torch.tensor(5.0, dtype=input.dtype)
+
+    if alpha is None:
+        alpha = torch.tensor(5.0, dtype=input.dtype)
+
+    return epsilon * (1.0 - torch.exp(-alpha * (input - sigma))) ** 2.0 - epsilon