def calculate(self, name, atoms):
data = HaasTranBlahaBulkTask.calculate(self, name, atoms)
sforces = - atoms.get_stress().ravel()*atoms.get_volume()
rmssforces = np.sum(sforces**2)**0.5
data['relaxed fmax'] = rmssforces
data['fmax'] = self.sfmax
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.ssteps
if 'strain optimizer force calls' in data:
data['optimizer force calls'] = data['strain optimizer force calls']
if data['relaxed fmax'] > data['fmax']:
raise RuntimeError('Optimization failed to converge')
return data
def calculate(self, name, atoms):
data = HaasTranBlahaBulkTask.calculate(self, name, atoms)
sforces = -atoms.get_stress().ravel() * atoms.get_volume()
rmssforces = np.sum(sforces**2)**0.5
data['relaxed fmax'] = rmssforces
data['fmax'] = self.sfmax
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.ssteps
if 'strain optimizer force calls' in data:
data['optimizer force calls'] = data[
'strain optimizer force calls']
if data['relaxed fmax'] > data['fmax']:
raise RuntimeError('Optimization failed to converge')
return data
def __init__(self, xc='LDA', **kwargs):
HaasTranBlahaBulkTask.__init__(self, xc, **kwargs)
def __init__(self, xc='LDA', **kwargs):
HaasTranBlahaBulkTask.__init__(self, xc, **kwargs)
