# -*- coding: utf-8 -*-
"""Copyright 2019 DScribe developers
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import numpy as np
from ase import Atoms
from dscribe.core import System
from dscribe.descriptors.matrixdescriptor import MatrixDescriptor
[docs]class CoulombMatrix(MatrixDescriptor):
"""Calculates the zero padded Coulomb matrix.
The Coulomb matrix is defined as:
C_ij = 0.5 Zi**exponent | i = j
= (Zi*Zj)/(Ri-Rj) | i != j
The matrix is padded with invisible atoms, which means that the matrix is
padded with zeros until the maximum allowed size defined by n_max_atoms is
reached.
To reach invariance against permutation of atoms, specify a valid option
for the permutation parameter.
For reference, see:
"Fast and Accurate Modeling of Molecular Atomization Energies with
Machine Learning", Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert
Mueller, and O. Anatole von Lilienfeld, Phys. Rev. Lett, (2012),
https://doi.org/10.1103/PhysRevLett.108.058301
and
"Learning Invariant Representations of Molecules for Atomization Energy
Prediction", Gregoire Montavon et. al, Advances in Neural Information
Processing Systems 25 (NIPS 2012)
"""
[docs] def create(self, system, n_jobs=1, verbose=False):
"""Return the Coulomb matrix for the given systems.
Args:
system (:class:`ase.Atoms` or list of :class:`ase.Atoms`): One or
many atomic structures.
n_jobs (int): Number of parallel jobs to instantiate. Parallellizes
the calculation across samples. Defaults to serial calculation
with n_jobs=1.
verbose(bool): Controls whether to print the progress of each job
into to the console.
Returns:
np.ndarray | scipy.sparse.csr_matrix: Coulomb matrix for the given
systems. The return type depends on the 'sparse' and
'flatten'-attributes. For flattened output a single numpy array or
sparse scipy.csr_matrix is returned. The first dimension is
determined by the amount of systems.
"""
# If single system given, skip the parallelization
if isinstance(system, (Atoms, System)):
return self.create_single(system)
else:
self._check_system_list(system)
# Combine input arguments
inp = [(i_sys,) for i_sys in system]
# Here we precalculate the size for each job to preallocate memory.
if self._flatten:
n_samples = len(system)
k, m = divmod(n_samples, n_jobs)
jobs = (inp[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n_jobs))
output_sizes = [len(job) for job in jobs]
else:
output_sizes = None
# Create in parallel
output = self.create_parallel(inp, self.create_single, n_jobs, output_sizes, verbose=verbose)
return output
[docs] def get_matrix(self, system):
"""Creates the Coulomb matrix for the given system.
Args:
system (:class:`ase.Atoms` | :class:`.System`): Input system.
Returns:
np.ndarray: Coulomb matrix as a 2D array.
"""
# Make sure that the system is non-periodic
system.set_pbc(False)
# Calculate offdiagonals
q = system.get_atomic_numbers()
qiqj = q[None, :]*q[:, None]
idmat = system.get_inverse_distance_matrix()
np.fill_diagonal(idmat, 0)
cmat = qiqj*idmat
# Set diagonal
np.fill_diagonal(cmat, 0.5 * q ** 2.4)
return cmat