def test_ase(self):
from ase import Atoms
from deepmd.calculator import DP
water0 = Atoms('OHHOHH',
positions=self.coords.reshape((-1, 3)),
cell=self.box.reshape((3, 3)),
calculator=DP(FROZEN_MODEL))
water1 = Atoms('OHHOHH',
positions=self.coords.reshape((-1, 3)),
cell=self.box.reshape((3, 3)),
calculator=DP(COMPRESSED_MODEL))
ee0 = water0.get_potential_energy()
ff0 = water0.get_forces()
ee1 = water1.get_potential_energy()
ff1 = water1.get_forces()
nframes = 1
np.testing.assert_almost_equal(ff0, ff1, default_places)
np.testing.assert_almost_equal(ee0, ee1, default_places)
def test_ase(self):
from ase import Atoms
from deepmd.calculator import DP
water = Atoms('OHHOHH',
positions=self.coords.reshape((-1, 3)),
cell=self.box.reshape((3, 3)),
calculator=DP("deeppot.pb"))
ee = water.get_potential_energy()
ff = water.get_forces()
nframes = 1
np.testing.assert_almost_equal(ff.ravel(), self.expected_f.ravel(),
default_places)
expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1)
np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(),
default_places)
import time
from ase import Atoms
from ase.optimize import BFGS
from pprint import pprint
from deepmd.calculator import DP
from pymatgen.io.ase import AseAtomsAdaptor,Structure
from pyxtal.symmetry import get_symbol_and_number
from pymatgen.io.cif import CifWriter
from pymatgen.symmetry.analyzer import PointGroupAnalyzer
from pyxtal.crystal import random_crystal, random_crystal_1D, random_crystal_2D, random_cluster
from pymatgen import Molecule,Structure
import numpy as np
from monty.serialization import loadfn,dumpfn
import random
vac=15
calc=DP(model="frozen_model.pb",type_dict={'Au':0})
def gen_cluster(system,numIons,sg=None,dimension=0,factor=1.0):
#space group
random.seed(int(time.time()*1e5))
if sg:
pass
else:
sg=random.choice(range(1,57))
symbol, sg = get_symbol_and_number(sg, dimension)
numIons0 = np.array(numIons)
cluster = random_cluster(sg, system, numIons0, factor)
if cluster.valid:
comp = str(cluster.struct.composition)
comp = comp.replace(" ", "")
import os
from ase import Atoms
from ase.optimize import BFGS
from deepmd.calculator import DP
from pymatgen.io.ase import AseAtomsAdaptor, Structure
from pymatgen import Molecule, Structure
import numpy as np
from monty.serialization import loadfn, dumpfn
import random
st = Structure.from_file("rand_Cu16.vasp")
comp = str(st.composition)
comp = comp.replace(" ", "")
aaa = AseAtomsAdaptor()
atoms = aaa.get_atoms(st)
atoms.set_calculator(DP(model="frozen_model.pb", type_dict={'Cu': 0}))
dyn = BFGS(atoms)
#dyn = BFGS(atoms,trajectory=comp+'.traj')
dyn.run(fmax=1e-4)
aaa.get_structure(atoms)
opted = aaa.get_structure(atoms)
help='if using dp'
)
parser.add_argument(
'-g', '--graph',
default='graph.pb', help='deep potential'
)
args = parser.parse_args()
# calculate using dp
if args.calc:
frames = read(args.train, ':')
from deepmd.calculator import DP
calc = DP(
model = args.graph,
#type_dict = {"O": 1, "Zn": 2, "Cr": 0}
)
data_dict = {'ener': [[],[]], 'force': [[],[]]}
for atoms in frames:
natoms = len(atoms)
energy = atoms.get_potential_energy() / natoms
forces = atoms.get_forces()
data_dict['ener'][0].append(energy)
data_dict['force'][0].extend(forces.flatten().tolist())
# calculation
calc.reset()
atoms.calc = calc
energy = atoms.get_potential_energy() / natoms
forces = atoms.get_forces()
data_dict['ener'][1].append(energy)
data_dict['force'][1].extend(forces.flatten().tolist())