qmlearn.api package

Submodules

qmlearn.api.api4ase module

class qmlearn.api.api4ase.QMLCalculator(qmmodel=None, second_learn={}, method='gamma', label='QMLearn', atoms=None, directory='.', refqmmol=None, properties='energy', **kwargs)

Methods

calculate([atoms, properties, system_changes])

Do the calculation.

calc_with_engine
calc_with_gamma

calculate(atoms=None, properties='energy', system_changes=['positions', 'numbers', 'cell', 'pbc', 'initial_charges', 'initial_magmoms'])

Do the calculation.

properties: list of str

List of what needs to be calculated. Can be any combination of ‘energy’, ‘forces’, ‘stress’, ‘dipole’, ‘charges’, ‘magmom’ and ‘magmoms’.

system_changes: list of str

List of what has changed since last calculation. Can be any combination of these six: ‘positions’, ‘numbers’, ‘cell’, ‘pbc’, ‘initial_charges’ and ‘initial_magmoms’.

Subclasses need to implement this, but can ignore properties and system_changes if they want. Calculated properties should be inserted into results dictionary like shown in this dummy example:

self.results = {'energy': 0.0,
                'forces': np.zeros((len(atoms), 3)),
                'stress': np.zeros(6),
                'dipole': np.zeros(3),
                'charges': np.zeros(len(atoms)),
                'magmom': 0.0,
                'magmoms': np.zeros(len(atoms))}

The subclass implementation should first call this implementation to set the atoms attribute and create any missing directories.

qmlearn.api.constraints module

class qmlearn.api.constraints.FixBondLComb(pairs=None, coefs=None, dt=None, tol=1e-06, target=None, maxiter=1000, scale=2.0)

This is similar to ASE FixBondLengths, but with linear combination of bond lengths. sum_i(bond_length_i * coefs_i) = constant

Attributes

dt

Methods

adjust_forces
adjust_momenta
adjust_positions
copy
get_jacobian
get_prims
get_prims_vel
get_removed_dof
output

Module contents