# -*- 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.
"""
from warnings import warn
import numpy as np
from scipy.special import gamma
from scipy.linalg import sqrtm, inv
from ase import Atoms
import ase.geometry.cell
import ase.data
from dscribe.utils.species import get_atomic_numbers
from dscribe.descriptors.descriptorlocal import DescriptorLocal
import dscribe.ext
[docs]class SOAP(DescriptorLocal):
"""Class for generating a partial power spectrum from Smooth Overlap of
Atomic Orbitals (SOAP). This implementation uses real (tesseral) spherical
harmonics as the angular basis set and provides two orthonormalized
alternatives for the radial basis functions: spherical primitive gaussian
type orbitals ("gto") or the polynomial basis set ("polynomial").
For reference, see:
"On representing chemical environments, Albert P. Bartók, Risi Kondor, and
Gábor Csányi, Phys. Rev. B 87, 184115, (2013),
https://doi.org/10.1103/PhysRevB.87.184115
"Comparing molecules and solids across structural and alchemical space",
Sandip De, Albert P. Bartók, Gábor Csányi and Michele Ceriotti, Phys.
Chem. Chem. Phys. 18, 13754 (2016), https://doi.org/10.1039/c6cp00415f
"Machine learning hydrogen adsorption on nanoclusters through structural
descriptors", Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova,
Lauri Himanen & Adam S. Foster, npj Comput. Mater., 4, 37 (2018),
https://doi.org/10.1038/s41524-018-0096-5
"""
[docs] def __init__(
self,
r_cut=None,
n_max=None,
l_max=None,
sigma=1.0,
rbf="gto",
weighting=None,
average="off",
compression={"mode": "off", "species_weighting": None},
species=None,
periodic=False,
sparse=False,
dtype="float64",
):
"""
Args:
r_cut (float): A cutoff for local region in angstroms. Should be
bigger than 1 angstrom for the gto-basis.
n_max (int): The number of radial basis functions.
l_max (int): The maximum degree of spherical harmonics.
sigma (float): The standard deviation of the gaussians used to expand the
atomic density.
rbf (str): The radial basis functions to use. The available options are:
* ``"gto"``: Spherical gaussian type orbitals defined as :math:`g_{nl}(r) = \sum_{n'=1}^{n_\mathrm{max}}\,\\beta_{nn'l} r^l e^{-\\alpha_{n'l}r^2}`
* ``"polynomial"``: Polynomial basis defined as :math:`g_{n}(r) = \sum_{n'=1}^{n_\mathrm{max}}\,\\beta_{nn'} (r-r_\mathrm{cut})^{n'+2}`
weighting (dict): Contains the options which control the
weighting of the atomic density. Leave unspecified if
you do not wish to apply any weighting. The dictionary may
contain the following entries:
* ``"function"``: The weighting function to use. The
following are currently supported:
* ``"poly"``: :math:`w(r) = \left\{ \\begin{array}{ll} c(1 + 2 (\\frac{r}{r_0})^{3} -3 (\\frac{r}{r_0})^{2}))^{m}, \\ \\text{for}\\ r \\leq r_0\\\\ 0, \\ \\text{for}\\ r > r_0 \end{array}\\right.`
This function goes exactly to zero at :math:`r=r_0`. If
you do not explicitly provide ``r_cut`` in the
constructor, ``r_cut`` is automatically set to ``r0``.
You can provide the parameters ``c``, ``m`` and ``r0`` as
additional dictionary items.
For reference see:
"Caro, M. (2019). Optimizing many-body atomic
descriptors for enhanced computational performance of
machine learning based interatomic potentials. Phys.
Rev. B, 100, 024112."
* ``"pow"``: :math:`w(r) = \\frac{c}{d + (\\frac{r}{r_0})^{m}}`
If you do not explicitly provide ``r_cut`` in the
constructor, ``r_cut`` will be set as the value at which
this function decays to the value given by the
``threshold`` entry in the weighting dictionary (defaults
to 1e-2), You can provide the parameters ``c``, ``d``,
``m``, ``r0`` and ``threshold`` as additional dictionary
items.
For reference see:
"Willatt, M., Musil, F., & Ceriotti, M. (2018).
Feature optimization for atomistic machine learning
yields a data-driven construction of the periodic
table of the elements. Phys. Chem. Chem. Phys., 20,
29661-29668.
"
* ``"exp"``: :math:`w(r) = \\frac{c}{d + e^{-r/r_0}}`
If you do not explicitly provide ``r_cut`` in the
constructor, ``r_cut`` will be set as the value at which
this function decays to the value given by the
``threshold`` entry in the weighting dictionary (defaults
to 1e-2), You can provide the parameters ``c``, ``d``,
``r0`` and ``threshold`` as additional dictionary items.
* ``"w0"``: Optional weight for atoms that are directly on top
of a requested center. Setting this value to zero essentially
hides the central atoms from the output. If a weighting
function is also specified, this constant will override it
for the central atoms.
average (str): The averaging mode over the centers of interest.
Valid options are:
* ``"off"``: No averaging.
* ``"inner"``: Averaging over sites before summing up the magnetic quantum numbers: :math:`p_{nn'l}^{Z_1,Z_2} \sim \sum_m (\\frac{1}{n} \sum_i c_{nlm}^{i, Z_1})^{*} (\\frac{1}{n} \sum_i c_{n'lm}^{i, Z_2})`
* ``"outer"``: Averaging over the power spectrum of different sites: :math:`p_{nn'l}^{Z_1,Z_2} \sim \\frac{1}{n} \sum_i \sum_m (c_{nlm}^{i, Z_1})^{*} (c_{n'lm}^{i, Z_2})`
compression (dict): Contains the options which specify the feature compression to apply.
Applying compression can slightly reduce the accuracy of models trained on the feature
representation but can also dramatically reduce the size of the feature vector
and hence the computational cost. Options are:
* ``"mode"``: Specifies the type of compression. This can be one of:
* ``"off"``: No compression; default.
* ``"mu2"``: The SOAP feature vector is generated in an element-agnostic way, so that
the size of the feature vector is now independent of the number of elements (see Darby et al.
below for details). It is still possible when using this option to construct a feature
vector that distinguishes between elements by supplying element-specific weighting under
"species_weighting", see below.
* ``"mu1nu1"``: Implements the mu=1, nu=1 feature compression scheme from Darby et al.: :math:`p_{inn'l}^{Z_1,Z_2} \sum_m (c_{nlm}^{i, Z_1})^{*} (\sum_z c_{n'lm}^{i, z})`.
In other words, each coefficient for each species is multiplied by a "species-mu2" sum over the corresponding set of coefficients for all other species.
If this option is selected, features are generated for each center, but the number of features (the size of each feature vector) scales linearly rather than
quadratically with the number of elements in the system.
* ``"crossover"``: The power spectrum does not contain cross-species information
and is only run over each unique species Z. In this configuration, the size of
the feature vector scales linearly with the number of elements in the system.
* ``"species_weighting"``: Either ``None`` or a dictionary mapping each species to a
species-specific weight. If None, there is no species-specific weighting. If a dictionary,
must contain a matching key for each species in the ``species`` iterable.
The main use of species weighting is to weight each element differently when using
the "mu2" option for ``compression``.
For reference see:
"Darby, J.P., Kermode, J.R. & Csányi, G.
Compressing local atomic neighbourhood descriptors.
npj Comput Mater 8, 166 (2022). https://doi.org/10.1038/s41524-022-00847-y"
species (iterable): The chemical species as a list of atomic
numbers or as a list of chemical symbols. Notice that this is not
the atomic numbers that are present for an individual system, but
should contain all the elements that are ever going to be
encountered when creating the descriptors for a set of systems.
Keeping the number of chemical species as low as possible is
preferable.
periodic (bool): Set to True if you want the descriptor output to
respect the periodicity of the atomic systems (see the
pbc-parameter in the constructor of ase.Atoms).
sparse (bool): Whether the output should be a sparse matrix or a
dense numpy array.
dtype (str): The data type of the output. Valid options are:
* ``"float32"``: Single precision floating point numbers.
* ``"float64"``: Double precision floating point numbers.
"""
supported_dtype = set(("float32", "float64"))
if dtype not in supported_dtype:
raise ValueError(
"Invalid output data type '{}' given. Please use "
"one of the following: {}".format(dtype, supported_dtype)
)
super().__init__(periodic=periodic, sparse=sparse, dtype=dtype)
# Setup the involved chemical species
self.species = species
# Test that general settings are valid
if sigma <= 0:
raise ValueError(
"Only positive gaussian width parameters 'sigma' are allowed."
)
self._eta = 1 / (2 * sigma**2)
self._sigma = sigma
supported_rbf = {"gto", "polynomial"}
if rbf not in supported_rbf:
raise ValueError(
"Invalid radial basis function of type '{}' given. Please use "
"one of the following: {}".format(rbf, supported_rbf)
)
if n_max < 1:
raise ValueError(
"Must have at least one radial basis function." "n_max={}".format(n_max)
)
supported_average = set(("off", "inner", "outer"))
if average not in supported_average:
raise ValueError(
"Invalid average mode '{}' given. Please use "
"one of the following: {}".format(average, supported_average)
)
if not (weighting or r_cut):
raise ValueError("Either weighting or r_cut need to be defined")
if weighting:
w0 = weighting.get("w0")
if w0 is not None:
if w0 < 0:
raise ValueError("Define w0 > 0 in weighting.")
weighting["w0"] = float(w0)
func = weighting.get("function")
if func is not None:
weighting_functions = {"poly", "pow", "exp"}
if func not in weighting_functions:
raise ValueError(
"Weighting function not implemented. Please choose "
"among one of the following {}".format(weighting_functions)
)
r0 = weighting.get("r0")
if r0 is None or r0 <= 0:
raise ValueError("Define r0 > 0 in weighting.")
weighting["r0"] = float(r0)
c = weighting.get("c")
if c is None or c < 0:
raise ValueError("Define c >= 0 in weighting.")
weighting["c"] = float(c)
if func == "poly":
m = weighting.get("m")
if m is None or m < 0:
raise ValueError("Define m >= 0 in weighting.")
weighting["m"] = float(m)
elif func == "pow":
d = weighting.get("d")
if d is None or d < 0:
raise ValueError("Define d >= 0 in weighting.")
weighting["d"] = float(d)
m = weighting.get("m")
if m is None or m < 0:
raise ValueError("Define m >= 0 in weighting.")
weighting["m"] = float(m)
weighting["threshold"] = float(weighting.get("threshold", 1e-2))
elif func == "exp":
d = weighting.get("d")
if d < 0:
raise ValueError("Define d >= 0 in weighting.")
weighting["d"] = float(d)
weighting["threshold"] = float(weighting.get("threshold", 1e-2))
else:
weighting = {}
if not r_cut:
r_cut = self._infer_r_cut(weighting)
# Test that radial basis set specific settings are valid
if rbf == "gto":
if r_cut <= 1:
raise ValueError(
"When using the gaussian radial basis set (gto), the radial "
"cutoff should be bigger than 1 angstrom."
)
# Precalculate the alpha and beta constants for the GTO basis
self._alphas, self._betas = self.get_basis_gto(r_cut, n_max, l_max)
# Test l_max
if l_max < 0:
raise ValueError("l_max cannot be negative. l_max={}".format(l_max))
elif l_max > 20:
raise ValueError(
"The maximum available l_max for SOAP is currently 20, you have"
" requested l_max={}".format(l_max)
)
self._r_cut = float(r_cut)
self._weighting = weighting
self._n_max = n_max
self._l_max = l_max
self._rbf = rbf
self.average = average
self.compression = compression
[docs] def prepare_centers(self, system, centers=None):
"""Validates and prepares the centers for the C++ extension."""
# Check that the system does not have elements that are not in the list
# of atomic numbers
self.check_atomic_numbers(system.get_atomic_numbers())
# Check if periodic is valid
if self.periodic:
cell = system.get_cell()
if np.cross(cell[0], cell[1]).dot(cell[2]) == 0:
raise ValueError("System doesn't have cell to justify periodicity.")
# Setup the local positions
if centers is None:
list_positions = system.get_positions()
indices = np.arange(len(system))
else:
# Check validity of position definitions and create final cartesian
# position list
error = ValueError(
"The argument 'positions' should contain a non-empty "
"one-dimensional list of"
" atomic indices or a two-dimensional "
"list of cartesian coordinates with x, y and z components."
)
if not isinstance(centers, (list, tuple, np.ndarray)) or len(centers) == 0:
raise error
list_positions = []
indices = np.full(len(centers), -1, dtype=np.int64)
for idx, i in enumerate(centers):
if np.issubdtype(type(i), np.integer):
list_positions.append(system.get_positions()[i])
indices[idx] = i
elif isinstance(i, (list, tuple, np.ndarray)):
if len(i) != 3:
raise error
list_positions.append(i)
else:
raise error
return np.asarray(list_positions), indices
[docs] def get_cutoff_padding(self):
"""The radial cutoff is extended by adding a padding that depends on
the used used sigma value. The padding is chosen so that the gaussians
decay to the specified threshold value at the cutoff distance.
"""
threshold = 0.001
cutoff_padding = self._sigma * np.sqrt(-2 * np.log(threshold))
return cutoff_padding
def _infer_r_cut(self, weighting):
"""Used to determine an appropriate r_cut based on where the given
weighting function setup.
"""
if weighting["function"] == "pow":
t = weighting["threshold"]
m = weighting["m"]
c = weighting["c"]
d = weighting["d"]
r0 = weighting["r0"]
r_cut = r0 * (c / t - d) ** (1 / m)
return r_cut
elif weighting["function"] == "poly":
r0 = weighting["r0"]
return r0
elif weighting["function"] == "exp":
t = weighting["threshold"]
c = weighting["c"]
d = weighting["d"]
r0 = weighting["r0"]
r_cut = r0 * np.log(c / t - d)
return r_cut
else:
return None
[docs] def init_internal_dev_array(self, n_centers, n_atoms, n_types, n, l_max):
d = np.zeros(
(n_atoms, n_centers, n_types, n, (l_max + 1) * (l_max + 1)),
dtype=np.float64,
)
return d
[docs] def init_internal_array(self, n_centers, n_types, n, l_max):
d = np.zeros(
(n_centers, n_types, n, (l_max + 1) * (l_max + 1)), dtype=np.float64
)
return d
[docs] def create(
self, system, centers=None, n_jobs=1, only_physical_cores=False, verbose=False
):
"""Return the SOAP output for the given systems and given centers.
Args:
system (:class:`ase.Atoms` or list of :class:`ase.Atoms`): One or
many atomic structures.
centers (list): Centers where to calculate SOAP. Can be
provided as cartesian positions or atomic indices. If no
centers are defined, the SOAP output will be created for all
atoms in the system. When calculating SOAP for multiple
systems, provide the centers as a list for each system.
n_jobs (int): Number of parallel jobs to instantiate. Parallellizes
the calculation across samples. Defaults to serial calculation
with n_jobs=1. If a negative number is given, the used cpus
will be calculated with, n_cpus + n_jobs, where n_cpus is the
amount of CPUs as reported by the OS. With only_physical_cores
you can control which types of CPUs are counted in n_cpus.
only_physical_cores (bool): If a negative n_jobs is given,
determines which types of CPUs are used in calculating the
number of jobs. If set to False (default), also virtual CPUs
are counted. If set to True, only physical CPUs are counted.
verbose(bool): Controls whether to print the progress of each job
into to the console.
Returns:
np.ndarray | sparse.COO: The SOAP output for the given systems and
centers. The return type depends on the 'sparse'-attribute. The
first dimension is determined by the amount of centers and
systems and the second dimension is determined by the
get_number_of_features()-function. When multiple systems are
provided the results are ordered by the input order of systems and
their positions.
"""
# Validate input / combine input arguments
if isinstance(system, Atoms):
system = [system]
centers = [centers]
n_samples = len(system)
if centers is None:
inp = [(i_sys,) for i_sys in system]
else:
n_pos = len(centers)
if n_pos != n_samples:
raise ValueError(
"The given number of centers does not match the given "
"number of systems."
)
inp = list(zip(system, centers))
# Determine if the outputs have a fixed size
n_features = self.get_number_of_features()
static_size = None
if self.average != "off":
static_size = [n_features]
else:
if centers is None:
n_centers = len(inp[0][0])
else:
first_sample, first_pos = inp[0]
if first_pos is not None:
n_centers = len(first_pos)
else:
n_centers = len(first_sample)
def is_static():
for i_job in inp:
if centers is None:
if len(i_job[0]) != n_centers:
return False
else:
if i_job[1] is not None:
if len(i_job[1]) != n_centers:
return False
else:
if len(i_job[0]) != n_centers:
return False
return True
if is_static():
static_size = [n_centers, n_features]
# Create in parallel
output = self.create_parallel(
inp,
self.create_single,
n_jobs,
static_size,
only_physical_cores,
verbose=verbose,
)
return output
[docs] def create_single(self, system, centers=None):
"""Return the SOAP output for the given system and given centers.
Args:
system (:class:`ase.Atoms` | :class:`.System`): Input system.
centers (list): Cartesian positions or atomic indices. If
specified, the SOAP spectrum will be created for these points.
If no centers are defined, the SOAP output will be created
for all atoms in the system.
Returns:
np.ndarray | sparse.COO: The SOAP output for the
given system and centers. The return type depends on the
'sparse'-attribute. The first dimension is given by the number of
centers and the second dimension is determined by the
get_number_of_features()-function.
"""
cutoff_padding = self.get_cutoff_padding()
centers, _ = self.prepare_centers(system, centers)
n_centers = centers.shape[0]
pos = system.get_positions()
Z = system.get_atomic_numbers()
soap_mat = self.init_descriptor_array(n_centers)
# Determine the function to call based on rbf
if self._rbf == "gto":
# Orthonormalized RBF coefficients
alphas = self._alphas.flatten()
betas = self._betas.flatten()
# Calculate with extension
soap_gto = dscribe.ext.SOAPGTO(
self._r_cut,
self._n_max,
self._l_max,
self._eta,
self._weighting,
self.average,
cutoff_padding,
self._atomic_numbers,
self._species_weights,
self.periodic,
self.compression["mode"],
alphas,
betas,
)
# Calculate analytically with extension
soap_gto.create(
soap_mat,
pos,
Z,
ase.geometry.cell.complete_cell(system.get_cell()),
np.asarray(system.get_pbc(), dtype=bool),
centers,
)
elif self._rbf == "polynomial":
# Get the discretized and orthogonalized polynomial radial basis
# function values
rx, gss = self.get_basis_poly(self._r_cut, self._n_max)
gss = gss.flatten()
# Calculate with extension
soap_poly = dscribe.ext.SOAPPolynomial(
self._r_cut,
self._n_max,
self._l_max,
self._eta,
self._weighting,
self.average,
cutoff_padding,
self._atomic_numbers,
self._species_weights,
self.periodic,
self.compression["mode"],
rx,
gss,
)
soap_poly.create(
soap_mat,
pos,
Z,
ase.geometry.cell.complete_cell(system.get_cell()),
np.asarray(system.get_pbc(), dtype=bool),
centers,
)
# Averaged output is a global descriptor, and thus the first dimension
# is squeezed out to keep the output size consistent with the size of
# other global descriptors.
if self.average != "off":
soap_mat = np.squeeze(soap_mat, axis=0)
return soap_mat
[docs] def validate_derivatives_method(self, method, attach):
"""Used to validate and determine the final method for calculating the
derivatives.
"""
methods = {"numerical", "analytical", "auto"}
if method not in methods:
raise ValueError(
"Invalid method specified. Please choose from: {}".format(methods)
)
if method == "numerical":
return method
# Check if analytical derivatives can be used
try:
if self._rbf == "polynomial":
raise ValueError(
"Analytical derivatives currently not available for polynomial "
"radial basis functions."
)
if self.average != "off":
raise ValueError(
"Analytical derivatives currently not available for averaged output."
)
if self.compression["mode"] not in ["off", "crossover"]:
raise ValueError(
"Analytical derivatives not currently available for mu1nu1, mu2 compression."
)
if self.periodic:
raise ValueError(
"Analytical derivatives currently not available for periodic systems."
)
if self._weighting:
raise ValueError(
"Analytical derivatives currently not available when weighting is used."
)
except Exception as e:
if method == "analytical":
raise e
elif method == "auto":
method = "numerical"
else:
if method == "auto":
method = "analytical"
return method
[docs] def derivatives_numerical(
self,
d,
c,
system,
centers,
indices,
attach,
return_descriptor=True,
):
"""Return the numerical derivatives for the given system.
Args:
system (:class:`ase.Atoms`): Atomic structure.
indices (list): Indices of atoms for which the derivatives will be
computed for.
return_descriptor (bool): Whether to also calculate the descriptor
in the same function call. This is true by default as it
typically is faster to calculate both in one go.
Returns:
If return_descriptor is True, returns a tuple, where the first item
is the derivative array and the second is the descriptor array.
Otherwise only returns the derivatives array. The derivatives array
is a 3D numpy array. The dimensions are: [n_atoms, 3, n_features].
The first dimension goes over the included atoms. The order is same
as the order of atoms in the given system. The second dimension
goes over the cartesian components, x, y and z. The last dimension
goes over the features in the default order.
"""
pos = system.get_positions()
Z = system.get_atomic_numbers()
cell = ase.geometry.cell.complete_cell(system.get_cell())
pbc = np.asarray(system.get_pbc(), dtype=bool)
cutoff_padding = self.get_cutoff_padding()
centers, center_indices = self.prepare_centers(system, centers)
if self._rbf == "gto":
alphas = self._alphas.flatten()
betas = self._betas.flatten()
soap_gto = dscribe.ext.SOAPGTO(
self._r_cut,
self._n_max,
self._l_max,
self._eta,
self._weighting,
self.average,
cutoff_padding,
self._atomic_numbers,
self._species_weights,
self.periodic,
self.compression["mode"],
alphas,
betas,
)
# Calculate numerically with extension
soap_gto.derivatives_numerical(
d,
c,
pos,
Z,
cell,
pbc,
centers,
center_indices,
indices,
attach,
return_descriptor,
)
elif self._rbf == "polynomial":
rx, gss = self.get_basis_poly(self._r_cut, self._n_max)
gss = gss.flatten()
# Calculate numerically with extension
soap_poly = dscribe.ext.SOAPPolynomial(
self._r_cut,
self._n_max,
self._l_max,
self._eta,
self._weighting,
self.average,
cutoff_padding,
self._atomic_numbers,
self._species_weights,
self.periodic,
self.compression["mode"],
rx,
gss,
)
soap_poly.derivatives_numerical(
d,
c,
pos,
Z,
ase.geometry.cell.complete_cell(system.get_cell()),
np.asarray(system.get_pbc(), dtype=bool),
centers,
center_indices,
indices,
attach,
return_descriptor,
)
[docs] def derivatives_analytical(
self,
d,
c,
system,
centers,
indices,
attach,
return_descriptor=True,
):
"""Return the analytical derivatives for the given system.
Args:
system (:class:`ase.Atoms`): Atomic structure.
indices (list): Indices of atoms for which the derivatives will be
computed for.
return_descriptor (bool): Whether to also calculate the descriptor
in the same function call. This is true by default as it
typically is faster to calculate both in one go.
Returns:
If return_descriptor is True, returns a tuple, where the first item
is the derivative array and the second is the descriptor array.
Otherwise only returns the derivatives array. The derivatives array
is a 3D numpy array. The dimensions are: [n_atoms, 3, n_features].
The first dimension goes over the included atoms. The order is same
as the order of atoms in the given system. The second dimension
goes over the cartesian components, x, y and z. The last dimension
goes over the features in the default order.
"""
pos = system.get_positions()
Z = system.get_atomic_numbers()
cell = ase.geometry.cell.complete_cell(system.get_cell())
pbc = np.asarray(system.get_pbc(), dtype=bool)
cutoff_padding = self.get_cutoff_padding()
centers, center_indices = self.prepare_centers(system, centers)
sorted_species = self._atomic_numbers
n_species = len(sorted_species)
n_centers = centers.shape[0]
n_atoms = len(system)
alphas = self._alphas.flatten()
betas = self._betas.flatten()
soap_gto = dscribe.ext.SOAPGTO(
self._r_cut,
self._n_max,
self._l_max,
self._eta,
self._weighting,
self.average,
cutoff_padding,
self._atomic_numbers,
self._species_weights,
self.periodic,
self.compression["mode"],
alphas,
betas,
)
# These arrays are only used internally by the C++ code.
# Allocating them here with python is much faster than
# allocating similarly sized arrays within C++. It seems
# that numpy does some kind of lazy allocation that is
# highly efficient for zero-initialized arrays. Similar
# performace could not be achieved even with calloc.
xd = self.init_internal_dev_array(
n_centers, n_atoms, n_species, self._n_max, self._l_max
)
yd = self.init_internal_dev_array(
n_centers, n_atoms, n_species, self._n_max, self._l_max
)
zd = self.init_internal_dev_array(
n_centers, n_atoms, n_species, self._n_max, self._l_max
)
soap_gto.derivatives_analytical(
d,
c,
xd,
yd,
zd,
pos,
Z,
cell,
pbc,
centers,
center_indices,
indices,
attach,
return_descriptor,
)
@property
def species(self):
return self._species
@species.setter
def species(self, value):
"""Used to check the validity of given atomic numbers and to initialize
the C-memory layout for them.
Args:
value(iterable): Chemical species either as a list of atomic
numbers or list of chemical symbols.
"""
# The species are stored as atomic numbers for internal use.
self._set_species(value)
# Setup mappings between atom indices and types
self.atomic_number_to_index = {}
self.index_to_atomic_number = {}
for i_atom, atomic_number in enumerate(self._atomic_numbers):
self.atomic_number_to_index[atomic_number] = i_atom
self.index_to_atomic_number[i_atom] = atomic_number
self.n_elements = len(self._atomic_numbers)
@property
def compression(self):
return self._compression
@compression.setter
def compression(self, value):
"""Used to check the validity of compression species weighting and set it up.
Note that species must already be set up in order to set species
weighting.
Args:
value(iterable): Chemical species either as a list of atomic
numbers or list of chemical symbols.
"""
# Check mode
supported_modes = set(("off", "mu2", "mu1nu1", "crossover"))
mode = value.get("mode", "off")
if mode not in supported_modes:
raise ValueError(
"Invalid compression mode '{}' given. Please use "
"one of the following: {}".format(
mode, supported_modes
)
)
# Check species weighting
species_weighting = value.get("species_weighting")
if species_weighting is None:
self._species_weights = np.ones((self.n_elements))
else:
if not isinstance(species_weighting, dict):
raise ValueError(
"Invalid species weighting '{}' given. Species weighting must "
"be either None or a dict.".format(value)
)
if len(species_weighting) != self.n_elements:
raise ValueError(
"The species_weighting dictionary, "
"if supplied, must contain the same keys as "
"the list of accepted species."
)
species_weights = []
for specie in list(self.species):
if specie not in species_weighting:
raise ValueError(
"The species_weighting dictionary, "
"if supplied, must contain the same keys as "
"the list of accepted species."
)
if isinstance(specie, (int, np.integer)):
if specie <= 0:
raise ValueError(
"Species weighting {} contained a zero or negative "
"atomic number.".format(species_weighting)
)
species_weights.append((species_weighting[specie], specie))
else:
species_weights.append(
(species_weighting[specie], ase.data.atomic_numbers.get(specie))
)
species_weights = [
s[0] for s in sorted(species_weights, key=lambda x: x[1])
]
self._species_weights = np.array(species_weights).astype(np.float64)
self._compression = value
[docs] def get_number_of_features(self):
"""Used to inquire the final number of features that this descriptor
will have.
Returns:
int: Number of features for this descriptor.
"""
n_elem = len(self._atomic_numbers)
if self.compression["mode"] == "mu2":
return int((self._n_max) * (self._n_max + 1) * (self._l_max + 1) / 2)
elif self.compression["mode"] == "mu1nu1":
return int(self._n_max**2 * n_elem * (self._l_max + 1))
elif self.compression["mode"] == "crossover":
return int(n_elem * self._n_max * (self._n_max + 1) / 2 * (self._l_max + 1))
n_elem_radial = n_elem * self._n_max
return int((n_elem_radial) * (n_elem_radial + 1) / 2 * (self._l_max + 1))
[docs] def get_location(self, species):
"""Can be used to query the location of a species combination in the
the flattened output.
Args:
species(tuple): A tuple containing a pair of species as chemical
symbols or atomic numbers. The tuple can be for example ("H", "O").
Returns:
slice: slice containing the location of the specified species
combination. The location is given as a python slice-object, that
can be directly used to target ranges in the output.
Raises:
ValueError: If the requested species combination is not in the
output or if invalid species defined.
"""
# Check that the corresponding part is calculated
if len(species) != 2:
raise ValueError("Please use a pair of atomic numbers or chemical symbols.")
# Change chemical elements into atomic numbers
numbers = []
for specie in species:
if isinstance(specie, str):
try:
specie = ase.data.atomic_numbers[specie]
except KeyError:
raise ValueError("Invalid chemical species: {}".format(specie))
numbers.append(specie)
# See if species defined
for number in numbers:
if number not in self._atomic_number_set:
raise ValueError(
"Atomic number {} was not specified in the species.".format(number)
)
# Change into internal indexing
numbers = [self.atomic_number_to_index[x] for x in numbers]
n_elem = self.n_elements
if numbers[0] > numbers[1]:
numbers = list(reversed(numbers))
i = numbers[0]
j = numbers[1]
if self.compression["mode"] == "off":
n_elem_feat_symm = self._n_max * (self._n_max + 1) / 2 * (self._l_max + 1)
n_elem_feat_unsymm = self._n_max * self._n_max * (self._l_max + 1)
n_elem_feat = n_elem_feat_symm if i == j else n_elem_feat_unsymm
# The diagonal terms are symmetric and off-diagonal terms are
# unsymmetric
m_symm = i + int(j > i)
m_unsymm = j + i * n_elem - i * (i + 1) / 2 - m_symm
start = int(m_symm * n_elem_feat_symm + m_unsymm * n_elem_feat_unsymm)
end = int(start + n_elem_feat)
elif self.compression["mode"] == "mu2":
n_elem_feat_symm = self._n_max * (self._n_max + 1) * (self._l_max + 1) / 2
start = 0
end = int(0 + n_elem_feat_symm)
elif self.compression["mode"] in ["mu1nu1", "crossover"]:
n_elem_feat_symm = self._n_max**2 * (self._l_max + 1)
if self.compression["mode"] == "crossover":
n_elem_feat_symm = (
self._n_max * (self._n_max + 1) * (self._l_max + 1) / 2
)
if i != j:
raise ValueError(
"Compression has been selected. "
"No cross-species output available"
)
start = int(i * n_elem_feat_symm)
end = int(start + n_elem_feat_symm)
return slice(start, end)
[docs] def get_basis_gto(self, r_cut, n_max, l_max):
"""Used to calculate the alpha and beta prefactors for the gto-radial
basis.
Args:
r_cut(float): Radial cutoff.
n_max(int): Number of gto radial bases.
Returns:
(np.ndarray, np.ndarray): The alpha and beta prefactors for all bases
up to a fixed size of l=10.
"""
# These are the values for where the different basis functions should decay
# to: evenly space between 1 angstrom and r_cut.
a = np.linspace(1, r_cut, n_max)
threshold = 1e-3 # This is the fixed gaussian decay threshold
alphas_full = np.zeros((l_max + 1, n_max))
betas_full = np.zeros((l_max + 1, n_max, n_max))
for l in range(0, l_max + 1):
# The alphas are calculated so that the GTOs will decay to the set
# threshold value at their respective cutoffs
alphas = -np.log(threshold / np.power(a, l)) / a**2
# Calculate the overlap matrix
m = np.zeros((alphas.shape[0], alphas.shape[0]))
m[:, :] = alphas
m = m + m.transpose()
S = 0.5 * gamma(l + 3.0 / 2.0) * m ** (-l - 3.0 / 2.0)
# Get the beta factors that orthonormalize the set with Löwdin
# orthonormalization
betas = sqrtm(inv(S))
# If the result is complex, the calculation is currently halted.
if betas.dtype == np.complex128:
raise ValueError(
"Could not calculate normalization factors for the radial "
"basis in the domain of real numbers. Lowering the number of "
"radial basis functions (n_max) or increasing the radial "
"cutoff (r_cut) is advised."
)
alphas_full[l, :] = alphas
betas_full[l, :, :] = betas
return alphas_full, betas_full
[docs] def get_basis_poly(self, r_cut, n_max):
"""Used to calculate discrete vectors for the polynomial basis functions.
Args:
r_cut(float): Radial cutoff.
n_max(int): Number of polynomial radial bases.
Returns:
(np.ndarray, np.ndarray): Tuple containing the evaluation points in
radial direction as the first item, and the corresponding
orthonormalized polynomial radial basis set as the second item.
"""
# Calculate the overlap of the different polynomial functions in a
# matrix S. These overlaps defined through the dot product over the
# radial coordinate are analytically calculable: Integrate[(rc - r)^(a
# + 2) (rc - r)^(b + 2) r^2, {r, 0, rc}]. Then the weights B that make
# the basis orthonormal are given by B=S^{-1/2}
S = np.zeros((n_max, n_max), dtype=np.float64)
for i in range(1, n_max + 1):
for j in range(1, n_max + 1):
S[i - 1, j - 1] = (2 * (r_cut) ** (7 + i + j)) / (
(5 + i + j) * (6 + i + j) * (7 + i + j)
)
# Get the beta factors that orthonormalize the set with Löwdin
# orthonormalization
betas = sqrtm(np.linalg.inv(S))
# If the result is complex, the calculation is currently halted.
if betas.dtype == np.complex128:
raise ValueError(
"Could not calculate normalization factors for the radial "
"basis in the domain of real numbers. Lowering the number of "
"radial basis functions (n_max) or increasing the radial "
"cutoff (r_cut) is advised."
)
# The radial basis is integrated in a very specific nonlinearly spaced
# grid given by rx
x = np.zeros(100)
x[0] = -0.999713726773441234
x[1] = -0.998491950639595818
x[2] = -0.996295134733125149
x[3] = -0.99312493703744346
x[4] = -0.98898439524299175
x[5] = -0.98387754070605702
x[6] = -0.97780935848691829
x[7] = -0.97078577576370633
x[8] = -0.962813654255815527
x[9] = -0.95390078292549174
x[10] = -0.94405587013625598
x[11] = -0.933288535043079546
x[12] = -0.921609298145333953
x[13] = -0.90902957098252969
x[14] = -0.895561644970726987
x[15] = -0.881218679385018416
x[16] = -0.86601468849716462
x[17] = -0.849964527879591284
x[18] = -0.833083879888400824
x[19] = -0.815389238339176254
x[20] = -0.79689789239031448
x[21] = -0.77762790964949548
x[22] = -0.757598118519707176
x[23] = -0.736828089802020706
x[24] = -0.715338117573056447
x[25] = -0.69314919935580197
x[26] = -0.670283015603141016
x[27] = -0.64676190851412928
x[28] = -0.622608860203707772
x[29] = -0.59784747024717872
x[30] = -0.57250193262138119
x[31] = -0.546597012065094168
x[32] = -0.520158019881763057
x[33] = -0.493210789208190934
x[34] = -0.465781649773358042
x[35] = -0.437897402172031513
x[36] = -0.409585291678301543
x[37] = -0.380872981624629957
x[38] = -0.351788526372421721
x[39] = -0.322360343900529152
x[40] = -0.292617188038471965
x[41] = -0.26258812037150348
x[42] = -0.23230248184497397
x[43] = -0.201789864095735997
x[44] = -0.171080080538603275
x[45] = -0.140203137236113973
x[46] = -0.109189203580061115
x[47] = -0.0780685828134366367
x[48] = -0.046871682421591632
x[49] = -0.015628984421543083
x[50] = 0.0156289844215430829
x[51] = 0.046871682421591632
x[52] = 0.078068582813436637
x[53] = 0.109189203580061115
x[54] = 0.140203137236113973
x[55] = 0.171080080538603275
x[56] = 0.201789864095735997
x[57] = 0.23230248184497397
x[58] = 0.262588120371503479
x[59] = 0.292617188038471965
x[60] = 0.322360343900529152
x[61] = 0.351788526372421721
x[62] = 0.380872981624629957
x[63] = 0.409585291678301543
x[64] = 0.437897402172031513
x[65] = 0.465781649773358042
x[66] = 0.49321078920819093
x[67] = 0.520158019881763057
x[68] = 0.546597012065094168
x[69] = 0.572501932621381191
x[70] = 0.59784747024717872
x[71] = 0.622608860203707772
x[72] = 0.64676190851412928
x[73] = 0.670283015603141016
x[74] = 0.693149199355801966
x[75] = 0.715338117573056447
x[76] = 0.736828089802020706
x[77] = 0.75759811851970718
x[78] = 0.77762790964949548
x[79] = 0.79689789239031448
x[80] = 0.81538923833917625
x[81] = 0.833083879888400824
x[82] = 0.849964527879591284
x[83] = 0.866014688497164623
x[84] = 0.881218679385018416
x[85] = 0.89556164497072699
x[86] = 0.90902957098252969
x[87] = 0.921609298145333953
x[88] = 0.933288535043079546
x[89] = 0.94405587013625598
x[90] = 0.953900782925491743
x[91] = 0.96281365425581553
x[92] = 0.970785775763706332
x[93] = 0.977809358486918289
x[94] = 0.983877540706057016
x[95] = 0.98898439524299175
x[96] = 0.99312493703744346
x[97] = 0.99629513473312515
x[98] = 0.998491950639595818
x[99] = 0.99971372677344123
rx = r_cut * 0.5 * (x + 1)
# Calculate the value of the orthonormalized polynomial basis at the rx
# values
fs = np.zeros([n_max, len(x)])
for n in range(1, n_max + 1):
fs[n - 1, :] = (r_cut - np.clip(rx, 0, r_cut)) ** (n + 2)
gss = np.dot(betas, fs)
return rx, gss