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")
class GAPotentialTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.this_dir = os.path.dirname(os.path.abspath(__file__))
cls.test_dir = tempfile.mkdtemp()
os.chdir(cls.test_dir)
@classmethod
def tearDownClass(cls):
os.chdir(CWD)
shutil.rmtree(cls.test_dir)
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 test_write_read_cfgs(self):
self.potential.write_cfgs("test.xyz", cfg_pool=self.test_pool)
datapool, df = self.potential.read_cfgs("test.xyz")
self.assertEqual(len(self.test_pool), len(datapool))
for data1, data2 in zip(self.test_pool, datapool):
struct1 = data1["structure"]
struct2 = Structure.from_dict(data2["structure"])
self.assertTrue(struct1 == struct2)
energy1 = data1["outputs"]["energy"]
energy2 = data2["outputs"]["energy"]
self.assertAlmostEqual(energy1, energy2)
forces1 = np.array(data1["outputs"]["forces"])
forces2 = data2["outputs"]["forces"]
np.testing.assert_array_almost_equal(forces1, forces2)
stress1 = np.array(data1["outputs"]["virial_stress"])
stress2 = data2["outputs"]["virial_stress"]
np.testing.assert_array_almost_equal(stress1, stress2)
@unittest.skipIf(not which("gap_fit"), "No QUIP cmd found.")
def test_train(self):
self.potential.train(
train_structures=self.test_structures,
train_energies=self.test_energies,
train_forces=self.test_forces,
train_stresses=self.test_stresses,
)
self.assertTrue(self.potential.param)
@unittest.skipIf(not which("quip"), "No QUIP cmd found.")
def test_evaluate(self):
self.potential.train(
train_structures=self.test_structures,
train_energies=self.test_energies,
train_forces=self.test_forces,
train_stresses=self.test_stresses,
)
df_orig, df_tar = self.potential.evaluate(
test_structures=self.test_structures,
test_energies=self.test_energies,
test_forces=self.test_forces,
test_stresses=self.test_stresses,
)
self.assertEqual(df_orig.shape[0], df_tar.shape[0])
@unittest.skipIf(not which("gap_fit"), "No QUIP cmd found.")
@unittest.skipIf(not which("lmp_serial"), "No LAMMPS cmd found.")
def test_predict(self):
self.potential.train(
train_structures=self.test_structures,
train_energies=self.test_energies,
train_forces=self.test_forces,
train_stresses=self.test_stresses,
)
energy, forces, stress = self.potential.predict_efs(self.test_struct)
self.assertEqual(len(forces), len(self.test_struct))
self.assertEqual(len(stress), 6)