def evaluate(self, test_structures, ref_energies, ref_forces,
ref_stresses):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
"""
predict_pool = pool_from(test_structures, ref_energies, ref_forces,
ref_stresses)
_, df_orig = convert_docs(predict_pool)
data_pool = []
for struct in test_structures:
d = {'outputs': {}}
d['structure'] = struct.as_dict()
d['num_atoms'] = len(struct)
features = self.describer.describe(struct)
targets = self.predictor.predict(features.values)
d['outputs']['energy'] = 0
d['outputs']['forces'] = targets.reshape((-1, 3))
d['outputs']['virial_stress'] = [0., 0., 0., 0., 0., 0.]
data_pool.append(d)
_, df_pred = convert_docs(data_pool)
return df_orig, df_pred
def evaluate(self, test_structures, ref_energies, ref_forces,
ref_stresses):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
"""
predict_pool = pool_from(test_structures, ref_energies, ref_forces,
ref_stresses)
_, df_orig = convert_docs(predict_pool)
_, df_predict = convert_docs(pool_from(test_structures))
outputs = self.model.predict(inputs=test_structures, override=True)
df_predict['y_orig'] = df_predict['n'] * outputs
return df_orig, df_predict
def train(self,
train_structures,
energies,
forces,
stresses=None,
**kwargs):
"""
Training data with model.
Args:
train_structures ([Structure]): The list of Pymatgen Structure object.
energies ([float]): The list of total energies of each structure
in structures list.
energies ([float]): List of total energies of each structure in
structures list.
forces ([np.array]): List of (m, 3) forces array of each structure
with m atoms in structures list. m can be varied with each
single structure case.
stresses (list): List of (6, ) virial stresses of each
structure in structures list.
"""
train_pool = pool_from(train_structures, energies, forces, stresses)
_, df = convert_docs(train_pool)
ytrain = df['y_orig'] / df['n']
self.model.fit(inputs=train_structures, outputs=ytrain, **kwargs)
self.specie = Element(train_structures[0].symbol_set[0])
def test_convert_docs(self):
_, df = convert_docs(self.test_pool, include_stress=False)
test_energies = df[df['dtype'] == 'energy']['y_orig']
self.assertFalse(np.any(test_energies - self.test_energies))
test_forces = df[df['dtype'] == 'force']['y_orig']
for force1, force2 in zip(test_forces,
np.array(self.test_forces).ravel()):
self.assertEqual(force1, force2)
_, df = convert_docs(self.test_pool, include_stress=True)
test_energies = df[df['dtype'] == 'energy']['y_orig']
self.assertFalse(np.any(test_energies - self.test_energies))
test_forces = df[df['dtype'] == 'force']['y_orig']
for force1, force2 in zip(test_forces,
np.array(self.test_forces).ravel()):
self.assertEqual(force1, force2)
test_stresses = df[df['dtype'] == 'stress']['y_orig']
for stress1, stress2 in zip(test_stresses,
np.array(self.test_stresses).ravel()):
self.assertEqual(stress1, stress2)
def evaluate2(self,
test_structures,
ref_energies=None,
ref_forces=None,
ref_stresses=None):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
"""
predict_pool = pool_from(test_structures, ref_energies, ref_forces,
ref_stresses)
_, df_orig = convert_docs(predict_pool)
efs_calculator = EnergyForceStress(ff_settings=self)
efs_results = efs_calculator.calculate(test_structures)
assert len(test_structures) == len(efs_results)
data_pool = []
for struct, (energy, forces, stresses) in zip(test_structures,
efs_results):
d = {'outputs': {}}
d['structure'] = struct.as_dict()
d['num_atoms'] = len(struct)
d['outputs']['energy'] = energy
d['outputs']['forces'] = forces
d['outputs']['virial_stress'] = stresses
data_pool.append(d)
_, df_pred = convert_docs(data_pool)
return df_orig, df_pred
def read_cfgs(self, filename, symbol):
"""
Args:
filename (str): The configuration file to be read.
symbol (str): The element symbol.
"""
data_pool = []
with zopen(filename, 'rt') as f:
lines = f.read()
block_pattern = re.compile('BEGIN_CFG\n(.*?)\nEND_CFG', re.S)
size_pattern = re.compile('Size\n(.*?)\n SuperCell', re.S | re.I)
lattice_pattern = re.compile('SuperCell\n(.*?)\n AtomData', re.S | re.I)
position_pattern = re.compile('fz\n(.*?)\n Energy', re.S)
energy_pattern = re.compile('Energy\n(.*?)\n Stress', re.S)
stress_pattern = re.compile('xy\n(.*?)(?=\n|$)', re.S)
def formatify(string):
return [float(s) for s in string.split()]
for block in block_pattern.findall(lines):
d = {'outputs': {}}
size_str = size_pattern.findall(block)[0]
size = int(size_str.lstrip())
lattice_str = lattice_pattern.findall(block)[0]
lattice = Lattice(np.array(list(map(formatify, lattice_str.split('\n')))))
position_str = position_pattern.findall(block)[0]
position = np.array(list(map(formatify, position_str.split('\n'))))
forces = position[:, 5:8].tolist()
position = position[:, 2:5]
energy_str = energy_pattern.findall(block)[0]
energy = float(energy_str.lstrip())
stress_str = stress_pattern.findall(block)[0]
virial_stress = np.array(list(map(formatify, stress_str.split()))).reshape(6, ).tolist()
virial_stress = [virial_stress[self.stress_order.index(n)]
for n in self.vasp_stress_order]
struct = Structure(lattice=lattice, species=[symbol] * size, coords=position,
coords_are_cartesian=True)
d['structure'] = struct.as_dict()
d['outputs']['energy'] = energy
assert size == struct.num_sites
d['num_atoms'] = size
d['outputs']['forces'] = forces
d['outputs']['virial_stress'] = virial_stress
data_pool.append(d)
_, df = convert_docs(docs=data_pool)
return data_pool, df
def read_cfgs(self, filename='output.data'):
"""
Args:
filename (str): The configuration file to be read.
"""
data_pool = []
with zopen(filename, 'rt') as f:
lines = f.read()
block_pattern = re.compile('begin\n(.*?)end', re.S)
lattice_pattern = re.compile('lattice(.*?)\n')
position_pattern = re.compile('atom(.*?)\n')
energy_pattern = re.compile('energy(.*?)\n')
for block in block_pattern.findall(lines):
d = {'outputs': {}}
lattice_str = lattice_pattern.findall(block)
lattice = Lattice(
np.array([latt.split() for latt in lattice_str],
dtype=np.float) * self.bohr_to_angstrom)
position_str = position_pattern.findall(block)
positions = pd.DataFrame([pos.split() for pos in position_str])
positions.columns = \
['x', 'y', 'z', 'specie', 'charge', 'atomic_energy', 'fx', 'fy', 'fz']
coords = np.array(positions.loc[:, ['x', 'y', 'z']],
dtype=np.float)
coords = coords * self.bohr_to_angstrom
species = np.array(positions['specie'])
forces = np.array(positions.loc[:, ['fx', 'fy', 'fz']],
dtype=np.float)
forces = forces / self.eV_to_Ha / self.bohr_to_angstrom
energy_str = energy_pattern.findall(block)[0]
energy = float(energy_str.lstrip()) / self.eV_to_Ha
struct = Structure(lattice=lattice,
species=species,
coords=coords,
coords_are_cartesian=True)
d['structure'] = struct.as_dict()
d['outputs']['energy'] = energy
d['outputs']['forces'] = forces
d['num_atoms'] = len(struct)
data_pool.append(d)
_, df = convert_docs(docs=data_pool)
return data_pool, df
def read_cfgs(self, filename, predict=False):
"""
Args:
filename (str): The configuration file to be read.
"""
type_convert = {'R': np.float32, 'I': np.int, 'S': np.str}
data_pool = []
with zopen(filename, 'rt') as f:
lines = f.read()
repl = re.compile('AT ')
lines = repl.sub('', string=lines)
block_pattern = re.compile(
'(\n[0-9]+\n|^[0-9]+\n)(.+?)(?=\n[0-9]+\n|$)', re.S)
lattice_pattern = re.compile('Lattice="(.+)"')
# energy_pattern = re.compile('dft_energy=(-?[0-9]+.[0-9]+)', re.I)
energy_pattern = re.compile(
r'(?<=\S{3}\s|dft_)energy=(-?[0-9]+.[0-9]+)')
# stress_pattern = re.compile('dft_virial={(.+)}')
stress_pattern = re.compile('dft_virial=({|)(.+?)(}|) \S.*')
properties_pattern = re.compile('properties=(\S+)', re.I)
# position_pattern = re.compile('\n(.+)', re.S)
position_pattern = re.compile('\n(.+?)(?=\nE.*|\n\n.*|$)', re.S)
# formatify = lambda string: [float(s) for s in string.split()]
for (size, block) in block_pattern.findall(lines):
d = {'outputs': {}}
size = int(size)
lattice_str = lattice_pattern.findall(block)[0]
lattice = Lattice(
list(map(lambda s: float(s), lattice_str.split())))
# energy_str = energy_pattern.findall(block)[0]
energy_str = energy_pattern.findall(block)[-1]
energy = float(energy_str)
# stress_str = stress_pattern.findall(block)[0]
stress_str = stress_pattern.findall(block)[0][1]
virial_stress = np.array(
list(map(lambda s: float(s), stress_str.split())))
virial_stress = [virial_stress[i] for i in [0, 4, 8, 1, 5, 6]]
properties = properties_pattern.findall(block)[0].split(":")
labels_columns = OrderedDict()
labels = defaultdict()
for i in range(0, len(properties), 3):
labels_columns[properties[i]] = [
int(properties[i + 2]), properties[i + 1]
]
position_str = position_pattern.findall(block)[0].split('\n')
position = np.array([p.split() for p in position_str])
column_index = 0
for key in labels_columns:
num_columns, dtype = labels_columns[key]
labels[key] = position[:, column_index:column_index +
num_columns].astype(type_convert[dtype])
column_index += num_columns
struct = Structure(lattice=lattice,
species=labels['species'].ravel(),
coords=labels['pos'],
coords_are_cartesian=True)
if predict:
forces = labels['force']
else:
forces = labels['dft_force']
d['structure'] = struct.as_dict()
d['outputs']['energy'] = energy
assert size == struct.num_sites
d['num_atoms'] = size
d['outputs']['forces'] = forces
d['outputs']['virial_stress'] = virial_stress
data_pool.append(d)
_, df = convert_docs(docs=data_pool)
return data_pool, df