def test_constructor(self):
"""Tests different valid and invalid constructor values.
"""
with self.assertRaises(ValueError):
SineMatrix(n_atoms_max=5, permutation="unknown")
with self.assertRaises(ValueError):
SineMatrix(n_atoms_max=-1)
def test_features(self):
"""Tests that the correct features are present in the desciptor.
"""
desc = SineMatrix(n_atoms_max=2, permutation="none", flatten=False)
# Test that without cell the matrix cannot be calculated
system = Atoms(
positions=[[0, 0, 0], [1.0, 1.0, 1.0]],
symbols=["H", "H"],
)
with self.assertRaises(ValueError):
desc.create(system)
# Test that periodic boundaries are considered by seeing that an atom
# in the origin is replicated to the corners
system = Atoms(
cell=[
[10, 10, 0],
[0, 10, 0],
[0, 0, 10],
],
scaled_positions=[[0, 0, 0], [1.0, 1.0, 1.0]],
symbols=["H", "H"],
pbc=True,
)
# from ase.visualize import view
# view(system)
matrix = desc.create(system)
# The interaction between atoms 1 and 2 should be infinite due to
# periodic boundaries.
self.assertEqual(matrix[0, 1], float("Inf"))
# The interaction of an atom with itself is always 0.5*Z**2.4
atomic_numbers = system.get_atomic_numbers()
for i, i_diag in enumerate(np.diag(matrix)):
self.assertEqual(i_diag, 0.5 * atomic_numbers[i]**2.4)
def test_flatten(self):
"""Tests the flattening."""
# Unflattened
desc = SineMatrix(n_atoms_max=5, permutation="none", flatten=False)
cm = desc.create(H2O)
self.assertEqual(cm.shape, (5, 5))
# Flattened
desc = SineMatrix(n_atoms_max=5, permutation="none", flatten=True)
cm = desc.create(H2O)
self.assertEqual(cm.shape, (25, ))
def test_exceptions(self):
"""Tests different invalid parameters that should raise an
exception.
"""
with self.assertRaises(ValueError):
SineMatrix(n_atoms_max=5, permutation="unknown")
with self.assertRaises(ValueError):
SineMatrix(n_atoms_max=-1)
with self.assertRaises(ValueError):
sm = SineMatrix(n_atoms_max=2)
sm.create([HHe, H2O])
def test_sparse(self):
"""Tests the sparse matrix creation."""
# Dense
desc = SineMatrix(n_atoms_max=5,
permutation="none",
flatten=True,
sparse=False)
vec = desc.create(H2O)
self.assertTrue(type(vec) == np.ndarray)
# Sparse
desc = SineMatrix(n_atoms_max=5,
permutation="none",
flatten=True,
sparse=True)
vec = desc.create(H2O)
self.assertTrue(type(vec) == sparse.COO)
def test_unit_cells(self):
"""Tests if arbitrary unit cells are accepted"""
desc = SineMatrix(n_atoms_max=3, permutation="none", flatten=False)
molecule = H2O.copy()
molecule.set_cell([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
with self.assertRaises(ValueError):
nocell = desc.create(molecule)
molecule.set_pbc(True)
molecule.set_cell([[20.0, 0.0, 0.0], [0.0, 30.0, 0.0],
[0.0, 0.0, 40.0]])
largecell = desc.create(molecule)
molecule.set_cell([[2.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 2.0]])
cubic_cell = desc.create(molecule)
molecule.set_cell([[0.0, 2.0, 2.0], [2.0, 0.0, 2.0], [2.0, 2.0, 0.0]])
triclinic_smallcell = desc.create(molecule)
"""
from functional.streams import ParallelStream as pseq
from collections import namedtuple
import ase.build.bulk
from dscribe.descriptors import CoulombMatrix
from dscribe.descriptors import SineMatrix
from dscribe.descriptors import EwaldMatrix
# Setup the descriptors
n_atoms_max = 4
n_proc = 4
coulombmatrix = CoulombMatrix(n_atoms_max=n_atoms_max)
sinematrix = SineMatrix(n_atoms_max=n_atoms_max)
ewaldmatrix = EwaldMatrix(n_atoms_max=n_atoms_max)
# Define a dataset
data = {
"NaCl": ase.build.bulk("NaCl", "rocksalt", 5.64),
"Diamond": ase.build.bulk("C", "diamond", 3.567),
"Al": ase.build.bulk("Al", "fcc", 4.046),
"GaAs": ase.build.bulk("GaAs", "zincblende", 5.653),
}
# Setup an iterable that runs through the samples.
Result = namedtuple("Result", "cm sm em")
Sample = namedtuple("Sample", "key value")
samples = [Sample(key, value) for key, value in data.items()]
# "https://wiki.fysik.dtu.dk/ase/ase/io/io.html" for a list of supported file
# formats.
atoms = ase.io.read("nacl.xyz")
atoms.set_cell([5.640200, 5.640200, 5.640200])
atoms.set_initial_charges(atoms.get_atomic_numbers())
# There are utilities for automatically detecting statistics for ASE Atoms
# objects. Typically some statistics are needed for the descriptors in order to
# e.g. define a proper zero-padding
stats = system_stats([atoms])
n_atoms_max = stats["n_atoms_max"]
atomic_numbers = stats["atomic_numbers"]
# Create descriptors for this system directly from the ASE atoms
cm = CoulombMatrix(n_atoms_max, permutation="sorted_l2").create(atoms)
sm = SineMatrix(n_atoms_max, permutation="sorted_l2").create(atoms)
mbtr = MBTR(atomic_numbers,
k=[1, 2, 3],
periodic=True,
weighting={
"k2": {
"function": "exponential",
"scale": 0.5,
"cutoff": 1e-3
},
"k3": {
"function": "exponential",
"scale": 0.5,
"cutoff": 1e-3
},
}).create(atoms)
from dscribe.descriptors import SineMatrix
# Setting up the sine matrix descriptor
sm = SineMatrix(n_atoms_max=6,
permutation="sorted_l2",
sparse=False,
flatten=True)
# Creation
from ase.build import bulk
# NaCl crystal created as an ASE.Atoms
nacl = bulk("NaCl", "rocksalt", a=5.64)
# Create output for the system
nacl_sine = sm.create(nacl)
# Create output for multiple system
al = bulk("Al", "fcc", a=4.046)
fe = bulk("Fe", "bcc", a=2.856)
samples = [nacl, al, fe]
sine_matrices = sm.create(samples) # Serial
sine_matrices = sm.create(samples, n_jobs=2) # Parallel
# Visualization
import numpy as np
from ase import Atoms
import matplotlib.pyplot as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable
# FCC aluminum crystal
from ase.spacegroup import crystal
from dscribe.descriptors import SineMatrix
# Define atomic structures
skutterudite = crystal(('Co', 'Sb'),
basis=[(0.25, 0.25, 0.25), (0.0, 0.335, 0.158)],
spacegroup=204,
cellpar=[9.04, 9.04, 9.04, 90, 90, 90])
nacl = bulk("NaCl", "rocksalt", a=5.64)
al = bulk("Al", "fcc", a=4.046)
fe = bulk("Fe", "bcc", a=2.856)
samples_bulk = [skutterudite, nacl, al, fe]
# Setup descriptor
sm_desc = SineMatrix(n_atoms_max=35,
permutation="sorted_l2",
sparse=False,
flatten=True)
# Create single descriptor
sine_matrix = sm_desc.create(skutterudite)
print("Sine matrix for skutterudite:\n", sine_matrix)
# Create multiple descriptors
sine_matrices = sm_desc.create(samples_bulk)
print("List of Sine matrices:\n", sine_matrices)
def test_parallel_dense(self):
"""Tests creating dense output parallelly.
"""
samples = [bulk("NaCl", "rocksalt", a=5.64), bulk('Cu', 'fcc', a=3.6)]
desc = SineMatrix(n_atoms_max=5,
permutation="none",
flatten=True,
sparse=False)
n_features = desc.get_number_of_features()
# Multiple systems, serial job
output = desc.create(
system=samples,
n_jobs=1,
)
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0])
assumed[1, :] = desc.create(samples[1])
self.assertTrue(np.allclose(output, assumed))
# Multiple systems, parallel job
output = desc.create(
system=samples,
n_jobs=2,
)
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0])
assumed[1, :] = desc.create(samples[1])
self.assertTrue(np.allclose(output, assumed))
# Non-flattened output
desc = SineMatrix(n_atoms_max=5,
permutation="none",
flatten=False,
sparse=False)
output = desc.create(
system=samples,
n_jobs=2,
)
assumed = np.empty((2, 5, 5))
assumed[0] = desc.create(samples[0])
assumed[1] = desc.create(samples[1])
self.assertTrue(np.allclose(np.array(output), assumed))
def test_number_of_features(self):
"""Tests that the reported number of features is correct.
"""
desc = SineMatrix(n_atoms_max=5, permutation="none", flatten=False)
n_features = desc.get_number_of_features()
self.assertEqual(n_features, 25)
def create(system):
desc = SineMatrix(n_atoms_max=3,
permutation="sorted_l2",
flatten=True)
return desc.create(system)