def __init__(self,
cutoff: float,
l_max: int = 8,
n_max: int = 8,
atom_sigma: float = 0.5,
feature_batch: str = 'pandas_concat',
**kwargs):
"""
Args:
cutoff (float): Cutoff radius.
l_max (int): The band limit of spherical harmonics basis function.
Default to 8.
n_max (int): The number of radial basis function. Default to 8.
atom_sigma (float): The width of gaussian atomic density. Default to 0.5.
feature_batch (str): way to batch together a list of features
**kwargs: keyword args to specify memory, verbose, and n_jobs
"""
from maml.apps.pes import GAPotential
self.operator = GAPotential()
self.cutoff = cutoff
self.l_max = l_max
self.n_max = n_max
self.atom_sigma = atom_sigma
super().__init__(feature_batch=feature_batch, **kwargs)
def fit_gap(atoms: List[Atoms],
energies: List[float],
forces: Optional[np.ndarray] = None,
**kwargs) -> Potential:
"""Fit a GAP potential using MAL and return a ready-to-use QUIPPy object
Args:
atoms: List of molecules to use for training
energies: List of energies for each structure
forces: If available, forces acting on each atom
output_dir: Directory in which to store potential
kwargs: Passed to the :meth:`GAPotential.train` method
Returns:
ase Potential object instantiated with a model fit to the data
"""
# Convert all of the structures to periodic
atoms = [make_periodic(a.copy()) for a in atoms]
# Conver them to PyMatgen objects
conv = AseAtomsAdaptor()
strcs = [conv.get_structure(a) for a in atoms]
# Fit using maml
gap = GAPotential()
gap.train(strcs, energies, forces, **kwargs)
# Save to disk and return the QUIPy object
gap.write_param()
return Potential(param_filename="gap.xml")
def setUp(self):
self.potential = GAPotential(name="test")
self.test_pool = test_datapool
self.test_structures = []
self.test_energies = []
self.test_forces = []
self.test_stresses = []
for d in self.test_pool:
self.test_structures.append(d["structure"])
self.test_energies.append(d["outputs"]["energy"])
self.test_forces.append(d["outputs"]["forces"])
self.test_stresses.append(d["outputs"]["virial_stress"])
self.test_struct = d["structure"]
def setUp(self):
self.potential = GAPotential(name='test')
self.test_pool = test_datapool
self.test_structures = []
self.test_energies = []
self.test_forces = []
self.test_stresses = []
for d in self.test_pool:
self.test_structures.append(d['structure'])
self.test_energies.append(d['outputs']['energy'])
self.test_forces.append(d['outputs']['forces'])
self.test_stresses.append(d['outputs']['virial_stress'])
self.test_struct = d['structure']