def calculate(self,
atoms=None,
properties=['energy'],
system_changes=all_changes):
Calculator.calculate(self, atoms, properties, system_changes)
self.write_input(self.atoms, properties, system_changes)
if self.command is None:
raise CalculatorSetupError('Please set ${} environment variable '.
format('ASE_' + self.name.upper() +
'_COMMAND') +
'or supply the command keyword')
command = self.command
if 'PREFIX' in command:
command = command.replace('PREFIX', self.prefix)
try:
proc = subprocess.Popen(command, shell=True, cwd=self.directory)
except OSError as err:
# Actually this may never happen with shell=True, since
# probably the shell launches successfully. But we soon want
# to allow calling the subprocess directly, and then this
# distinction (failed to launch vs failed to run) is useful.
msg = 'Failed to execute "{}"'.format(command)
raise EnvironmentError(msg) from err
errorcode = proc.wait()
if errorcode:
path = os.path.abspath(self.directory)
msg = ('Calculator "{}" failed with command "{}" failed in '
'{} with error code {}'.format(self.name, command, path,
errorcode))
raise CalculationFailed(msg)
self.read_results()
def calculate(self, atoms=None, properties=['energy'],
system_changes=all_changes):
Calculator.calculate(self, atoms, properties, system_changes)
self.write_input(self.atoms, properties, system_changes)
if self.command is None:
raise CalculatorSetupError(
'Please set ${} environment variable '
.format('ASE_' + self.name.upper() + '_COMMAND') +
'or supply the command keyword')
command = self.command.replace('PREFIX', self.prefix)
errorcode = subprocess.call(command, shell=True, cwd=self.directory)
if errorcode:
raise CalculationFailed('{} in {} returned an error: {}'
.format(self.name, self.directory,
errorcode))
self.read_results()
def calculate(self, atoms=None, properties=['energy'],
system_changes=all_changes):
Calculator.calculate(self, atoms, properties, system_changes)
self.write_input(self.atoms, properties, system_changes)
if self.command is None:
raise RuntimeError('Please set $%s environment variable ' %
('ASE_' + self.name.upper() + '_COMMAND') +
'or supply the command keyword')
command = self.command.replace('PREFIX', self.prefix)
olddir = os.getcwd()
try:
os.chdir(self.directory)
errorcode = subprocess.call(command, shell=True)
finally:
os.chdir(olddir)
if errorcode:
raise RuntimeError('%s returned an error: %d' %
(self.name, errorcode))
self.read_results()
def calculate(self, atoms=None, properties=['energy'],
system_changes=all_changes):
Calculator.calculate(self, atoms, properties, system_changes)
self.write_input(self.atoms, properties, system_changes)
if self.command is None:
raise RuntimeError('Please set $%s environment variable ' %
('ASE_' + self.name.upper() + '_COMMAND') +
'or supply the command keyword')
command = self.command.replace('PREFIX', self.prefix)
olddir = os.getcwd()
try:
os.chdir(self.directory)
errorcode = subprocess.call(command, shell=True)
finally:
os.chdir(olddir)
if errorcode:
raise RuntimeError('%s returned an error: %d' %
(self.name, errorcode))
self.read_results()
def calculate_numerical_forces(self, atoms, d=0.001, parallel=True):
"""Calculate numerical forces using finite difference.
All atoms will be displaced by +d and -d in all directions."""
if (str(self.__class__) == "<class 'amp.Amp'>"):
if (str(self.model.__class__) ==
"<class 'amp.model.tflow.NeuralNetwork'>"):
disp_atoms = self.generate_disp(atoms, d=d)
atomic_energies = self.calculateE_bunch(disp_atoms,
parallel=parallel)
atomic_energies = np.array(
np.split(
np.array(np.split(atomic_energies,
len(atoms) * 3)), len(atoms)))
forces = np.zeros((len(atoms), 3))
for i in range(len(atoms)):
for j in range(3):
forces [i][j] = \
-1 * ( atomic_energies[i][j][1] - atomic_energies[i][j][0] ) / (2 * d)
return forces
else:
Calculator.calculate(self, atoms)
disp_atoms = self.generate_disp(atoms, d=d)
log = self._log
log('Calculation requested.')
from amp.utilities import hash_images
images, seq_keys = hash_images(disp_atoms, list_seq=True)
keys = list(images.keys())[0]
############### load neighborlist file list most recently used
if hasattr(self, "neighborlist_keys"):
n_keys = self.neighborlist_keys
else:
n_keys = None
if hasattr(self, "fingerprints_keys"):
f_keys = self.fingerprints_keys
else:
f_keys = None
############# update neighborlists & fingerprints file lists
self.neighborlist_keys, self.fingerprints_keys = \
self.descriptor.calculate_fingerprints(
images=images,
log=log,
calculate_derivatives=False,
neighborlist_keys = n_keys,
fingerprints_keys = f_keys,
parallel = self._parallel if parallel else None,
)
log('Calculating potential energy...', tic='pot-energy')
seq_keys = np.array(
np.split(
np.array(np.split(np.array(seq_keys),
len(atoms) * 3)), len(atoms)))
forces = np.zeros((len(atoms), 3))
for a in range(len(atoms)):
for i in range(3):
eplus = self.model.calculate_energy(
self.descriptor.fingerprints[seq_keys[a][i][1]])
self.descriptor.fingerprints.close()
eminus = self.model.calculate_energy(
self.descriptor.fingerprints[seq_keys[a][i][0]])
self.descriptor.fingerprints.close()
forces[a][i] = (eminus - eplus) / (2 * d)
log('Potential energy calc ended...', toc='pot-energy')
return forces
else:
return np.array(
[[self.numeric_force(atoms, a, i, d) for i in range(3)]
for a in range(len(atoms))])
