def calculate(self, name, atoms):
data = MoleculeTask.calculate(self, name, atoms)
exx = HybridXC('EXX', alpha=5.0)
dexx = atoms.calc.get_xc_difference(exx)
data['EXX'] = dexx
data['EXXvc'] = exx.evc
return data
def calculate(self, name, atoms):
data = MoleculeTask.calculate(self, name, atoms)
try:
steps = atoms.get_calculator().get_number_of_iterations()
except (AttributeError, NotImplemented):
steps = None
data['calculator steps'] = steps
return data
def calculate(self, name, atoms):
data = MoleculeTask.calculate(self, name, atoms)
try:
steps = atoms.get_calculator().get_number_of_iterations()
except (AttributeError, NotImplemented):
steps = None
data['calculator steps'] = steps
return data
def calculate(self, name, atoms):
data = MoleculeTask.calculate(self, name, atoms)
data['relaxed fmax'] = np.sqrt((atoms.get_forces()**2).sum(axis=1)).max()
data['fmax'] = self.fmax
if 'energy' in data and 'relaxed energy' not in data:
# no optimization performed
data['relaxed energy'] = data['energy']
data['steps'] = 0
elif 'relaxed energy' in data:
# optimization
data['steps'] = self.steps
if data['relaxed fmax'] > data['fmax']:
raise RuntimeError('Optimization failed to converge')
return data
def calculate(self, name, atoms):
data = MoleculeTask.calculate(self, name, atoms)
data['relaxed fmax'] = np.sqrt(
(atoms.get_forces()**2).sum(axis=1)).max()
data['fmax'] = self.fmax
if 'energy' in data and 'relaxed energy' not in data:
# no optimization performed
data['relaxed energy'] = data['energy']
data['steps'] = 0
elif 'relaxed energy' in data:
# optimization
data['steps'] = self.steps
if data['relaxed fmax'] > data['fmax']:
raise RuntimeError('Optimization failed to converge')
return data
