def otf_object():
file_name = 'test_files/AgI_snippet.out'
# parse otf output
otf_traj = otf_parser.OtfAnalysis(file_name)
yield otf_traj
del otf_traj
import numpy as np
from flare import otf_parser
file_name = 'test_files/AgI_snippet.out'
hyp_no = 2 # use the hyperparameters from the 2nd training step
otf_object = otf_parser.OtfAnalysis(file_name)
# -------------------------------------------------------------------------
# reconstruct gp model from otf snippet
# -------------------------------------------------------------------------
gp_model = otf_object.make_gp(kernel_name="2+3_mc", hyp_no=hyp_no)
gp_model.parallel = True
gp_model.hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise']
# write model to a binary file
gp_model.write_model('AgI.gp', format='pickle')
# -------------------------------------------------------------------------
# map the potential
# -------------------------------------------------------------------------
from flare.mgp.mgp import MappedGaussianProcess
grid_num_2 = 64
grid_num_3 = 20
lower_cut = 2.5
two_cut = 7.
three_cut = 5.
lammps_location = 'AgI_Molten_15.txt'
# set struc params. cell and masses arbitrary?
mapped_cell = np.eye(3) * 100 # just use a sufficiently large one
def test_parse_header():
# -------------------------------------------------------------------------
# reconstruct gp model from otf snippet
# -------------------------------------------------------------------------
file_name = 'test_files/AgI_snippet.out'
hyp_no = 2
# parse otf output
otf_object = otf_parser.OtfAnalysis(file_name)
otf_cell = otf_object.header['cell']
# reconstruct gp model
kernel = mc_simple.two_plus_three_body_mc
kernel_grad = mc_simple.two_plus_three_body_mc_grad
gp_model = otf_object.make_gp(kernel=kernel,
kernel_grad=kernel_grad,
hyp_no=hyp_no)
gp_model.par = True
gp_model.hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise']
# -------------------------------------------------------------------------
# check gp reconstruction
# -------------------------------------------------------------------------
# create test structure
species = np.array([47, 53] * 27)
positions = otf_object.position_list[-1]
forces = otf_object.force_list[-1]
structure = struc.Structure(otf_cell, species, positions)
atom = 0
environ = env.AtomicEnvironment(structure, atom, gp_model.cutoffs)
force_comp = 2
pred, _ = gp_model.predict(environ, force_comp)
assert (np.isclose(pred, forces[0][1]))
# -------------------------------------------------------------------------
# map the potential
# -------------------------------------------------------------------------
file_name = 'AgI.gp'
grid_num_2 = 64
grid_num_3 = 15
lower_cut = 2.
two_cut = 7.
three_cut = 5.
lammps_location = 'AgI_Molten_15.txt'
# set struc params. cell and masses arbitrary?
mapped_cell = np.eye(3) * 100
struc_params = {
'species': [47, 53],
'cube_lat': mapped_cell,
'mass_dict': {
'0': 27,
'1': 16
}
}
# grid parameters
grid_params = {
'bounds_2': [[lower_cut], [two_cut]],
'bounds_3': [[lower_cut, lower_cut, 0], [three_cut, three_cut, np.pi]],
'grid_num_2': grid_num_2,
'grid_num_3': [grid_num_3, grid_num_3, grid_num_3],
'svd_rank_2': 64,
'svd_rank_3': 90,
'bodies': [2, 3],
'load_grid': None,
'update': True
}
mgp_model = MappedGaussianProcess(gp_model,
grid_params,
struc_params,
mean_only=True,
lmp_file_name=lammps_location)
# -------------------------------------------------------------------------
# test the mapped potential
# -------------------------------------------------------------------------
gp_pred_x = gp_model.predict(environ, 1)
mgp_pred = mgp_model.predict(environ, mean_only=True)
# check mgp is within 1 meV/A of the gp
assert (np.abs(mgp_pred[0][0] - gp_pred_x[0]) < 1e-3)
# -------------------------------------------------------------------------
# check lammps potential
# -------------------------------------------------------------------------
# mgp_model.write_lmp_file(lammps_location)
# lmp file is automatically written now every time MGP is constructed
# create test structure
species = otf_object.gp_species_list[-1]
positions = otf_object.position_list[-1]
forces = otf_object.force_list[-1]
structure = struc.Structure(otf_cell, species, positions)
atom_types = [1, 2]
atom_masses = [108, 127]
atom_species = [1, 2] * 27
# create data file
data_file_name = 'tmp.data'
data_text = lammps_calculator.lammps_dat(structure, atom_types,
atom_masses, atom_species)
lammps_calculator.write_text(data_file_name, data_text)
# create lammps input
style_string = 'mgp' #TODO: change the name of lammps
coeff_string = '* * {} 47 53 yes yes'.format(lammps_location)
lammps_executable = '$lmp'
dump_file_name = 'tmp.dump'
input_file_name = 'tmp.in'
output_file_name = 'tmp.out'
input_text = \
lammps_calculator.generic_lammps_input(data_file_name, style_string,
coeff_string, dump_file_name)
lammps_calculator.write_text(input_file_name, input_text)
lammps_calculator.run_lammps(lammps_executable, input_file_name,
output_file_name)
lammps_forces = lammps_calculator.lammps_parser(dump_file_name)
# check that lammps agrees with gp to within 1 meV/A
assert (np.abs(lammps_forces[0, 1] - forces[0, 1]) < 1e-3)
os.system('rm tmp.in tmp.out tmp.dump tmp.data AgI_Molten_15.txt'
' log.lammps')
os.system('rm grid3*.npy')
os.system('rm -r kv3')