def ase_calculator(gp_model, mgp_model):
"""
test the mapping for mc_simple kernel
"""
cal = FLARE_Calculator(gp_model, mgp_model, par=False, use_mapping=True)
yield cal
del cal
def flare_calc():
flare_calc_dict = {}
for md_engine in md_list:
# ---------- create gaussian process model -------------------
# set up GP hyperparameters
kernels = ["twobody", "threebody"] # use 2+3 body kernel
parameters = {"cutoff_twobody": 5.0, "cutoff_threebody": 3.5}
pm = ParameterHelper(kernels=kernels,
random=True,
parameters=parameters)
hm = pm.as_dict()
hyps = hm["hyps"]
cut = hm["cutoffs"]
print("hyps", hyps)
gp_model = GaussianProcess(
kernels=kernels,
component="sc", # single-component. For multi-comp, use 'mc'
hyps=hyps,
cutoffs=cut,
hyp_labels=["sig2", "ls2", "sig3", "ls3", "noise"],
opt_algorithm="L-BFGS-B",
n_cpus=1,
)
# ----------- create mapped gaussian process ------------------
grid_params = {
"twobody": {
"grid_num": [64]
},
"threebody": {
"grid_num": [16, 16, 16]
},
}
mgp_model = MappedGaussianProcess(grid_params=grid_params,
unique_species=[1, 2],
n_cpus=1,
var_map="pca")
# ------------ create ASE's flare calculator -----------------------
flare_calculator = FLARE_Calculator(gp_model,
mgp_model=mgp_model,
par=True,
use_mapping=True)
flare_calc_dict[md_engine] = flare_calculator
print(md_engine)
yield flare_calc_dict
del flare_calc_dict
def flare_calc():
flare_calc_dict = {}
for md_engine in md_list:
# ---------- create gaussian process model -------------------
# set up GP hyperparameters
kernels = ['twobody', 'threebody'] # use 2+3 body kernel
parameters = {'cutoff_twobody': 5.0, 'cutoff_threebody': 3.5}
pm = ParameterHelper(kernels=kernels,
random=True,
parameters=parameters)
hm = pm.as_dict()
hyps = hm['hyps']
cut = hm['cutoffs']
print('hyps', hyps)
gp_model = GaussianProcess(
kernels=kernels,
component='sc', # single-component. For multi-comp, use 'mc'
hyps=hyps,
cutoffs=cut,
hyp_labels=['sig2', 'ls2', 'sig3', 'ls3', 'noise'],
opt_algorithm='L-BFGS-B',
n_cpus=1)
# ----------- create mapped gaussian process ------------------
grid_params = {
'twobody': {
'grid_num': [64]
},
'threebody': {
'grid_num': [16, 16, 16]
}
}
mgp_model = MappedGaussianProcess(grid_params=grid_params,
unique_species=[1, 2],
n_cpus=1,
var_map='pca')
# ------------ create ASE's flare calculator -----------------------
flare_calculator = FLARE_Calculator(gp_model,
mgp_model=mgp_model,
par=True,
use_mapping=True)
flare_calc_dict[md_engine] = flare_calculator
print(md_engine)
yield flare_calc_dict
del flare_calc_dict
def test_otf_parser(md_engine):
output_name = f"{md_engine}.out"
otf_traj = OtfAnalysis(output_name)
try:
replicated_gp = otf_traj.make_gp()
except:
init_flare = FLARE_Calculator.from_file(md_engine + "_flare.json")
replicated_gp = otf_traj.make_gp(init_gp=init_flare.gp_model)
print("ase otf traj parsed")
for f in glob.glob(md_engine + "*"):
os.remove(f)
def test_build_map(all_gp, all_mgp, all_ase_calc, bodies, multihyps):
"""
test the mapping for mc_simple kernel
"""
# multihyps = False
gp_model = all_gp[f'{bodies}{multihyps}']
mgp_model = all_mgp[f'{bodies}{multihyps}']
mgp_model.build_map(gp_model)
all_ase_calc[f'{bodies}{multihyps}'] = FLARE_Calculator(gp_model,
mgp_model,
par=False,
use_mapping=True)
clean()
def test_otf_parser(md_engine):
output_name = f"{md_engine}.out"
otf_traj = OtfAnalysis(output_name)
try:
replicated_gp = otf_traj.make_gp()
except:
init_flare = FLARE_Calculator.from_file(md_engine + "_flare.json")
replicated_gp = otf_traj.make_gp(init_gp=init_flare.gp_model)
print("ase otf traj parsed")
# Check that the GP forces change.
comp1 = otf_traj.force_list[0][1, 0]
comp2 = otf_traj.force_list[-1][1, 0]
assert (comp1 != comp2)
for f in glob.glob(md_engine + "*"):
os.remove(f)
def test_otf_parser(md_engine, write_model):
output_name = f"{md_engine}_{write_model}"
otf_traj = OtfAnalysis(output_name + ".out")
try:
replicated_gp = otf_traj.make_gp()
except:
init_flare = FLARE_Calculator.from_file(output_name + "_flare.json")
replicated_gp = otf_traj.make_gp(init_gp=init_flare.gp_model)
print("ase otf traj parsed")
# Check that the GP forces change.
comp1 = otf_traj.force_list[-2][1, 0]
comp2 = otf_traj.force_list[-1][1, 0]
assert comp1 != comp2
for f in glob.glob(output_name + "*"):
os.remove(f)
for f in glob.glob("*_ckpt_*"):
shutil.rmtree(f, ignore_errors=True)
def from_dict(dct):
flare_calc_dict = json.load(open(dct["flare_calc"]))
# Build FLARE_Calculator from dict
if flare_calc_dict["class"] == "FLARE_Calculator":
flare_calc = FLARE_Calculator.from_file(dct["flare_calc"])
_kernels = None
# Build SGP_Calculator from dict
# TODO: we still have the issue that the c++ kernel needs to be
# in the current space, otherwise there is Seg Fault
# That's why there is the _kernels
elif flare_calc_dict["class"] == "SGP_Calculator":
from flare_pp.sparse_gp_calculator import SGP_Calculator
flare_calc, _kernels = SGP_Calculator.from_file(dct["flare_calc"])
else:
raise TypeError(
f"The calculator from {dct['flare_calc']} is not recognized."
)
flare_calc.reset()
dct["atoms"] = read(dct["atoms"])
dct["calculator"] = flare_calc
dct.pop("gp")
with open(dct["dft_loc"], "rb") as f:
dct["dft_calc"] = pickle.load(f)
for key in ["dt", "dft_loc"]:
dct.pop(key)
new_otf = ASE_OTF(**dct)
new_otf._kernels = _kernels
new_otf.dft_count = dct["dft_count"]
new_otf.curr_step = dct["curr_step"]
new_otf.std_tolerance = dct["std_tolerance"]
if new_otf.md_engine == "NPT":
if not new_otf.md.initialized:
new_otf.md.initialize()
return new_otf
def from_dict(dct):
flare_calc = FLARE_Calculator.from_file(dct["flare_calc"])
flare_calc.reset()
dct["atoms"] = read(dct["atoms"])
dct["atoms"].calc = flare_calc
dct.pop("gp")
with open(dct["dft_loc"], "rb") as f:
dct["dft_calc"] = pickle.load(f)
for key in ["dt", "dft_loc"]:
dct.pop(key)
new_otf = ASE_OTF(**dct)
new_otf.dft_count = dct["dft_count"]
new_otf.curr_step = dct["curr_step"]
if new_otf.md_engine == "NPT":
if not new_otf.md.initialized:
new_otf.md.initialize()
return new_otf
def from_dict(dct):
flare_calc = FLARE_Calculator.from_file(dct["flare_calc"])
# https://github.com/mir-group/flare/commit/88efe67e28f2b6a9cb5c6662675aad0209af134d
flare_calc.reset()
dct["atoms"] = read(dct["atoms"])
dct["atoms"].calc = flare_calc
dct.pop("gp")
with open(dct["dft_loc"], "rb") as f:
dct["dft_calc"] = pickle.load(f)
for key in ["dt", "dft_loc"]:
dct.pop(key)
new_otf = ASE_OTF(**dct)
new_otf.dft_count = dct["dft_count"]
new_otf.curr_step = dct["curr_step"]
new_otf.std_tolerance = dct["std_tolerance"]
if new_otf.md_engine == "NPT":
if not new_otf.md.initialized:
new_otf.md.initialize()
return new_otf
}
}
# grid parameters
lower_cut = 2.5
grid_num_2 = 8
grid_num_3 = 8
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': 0,
'svd_rank_3': 0,
'bodies': [2, 3],
'load_grid': None,
'update': True
}
mgp_model = MappedGaussianProcess(gp_model.hyps,
gp_model.cutoffs,
grid_params,
struc_params,
mean_only=False,
container_only=False,
GP=gp_model,
lmp_file_name='agi.mgp')
# ------------ create ASE's flare calculator -----------------------
flare_calc = FLARE_Calculator(gp_model, mgp_model, par=True, use_mapping=True)
def test_stress_with_lammps():
"""
Based on gp_test_al.out, ensures that given hyperparameters and DFT calls
a GP model can be reproduced and correctly re-predict forces and
uncertainties
:return:
"""
# build up GP from a previous trajectory
parsed = OtfAnalysis('test_files/VelocityVerlet.log')
positions = parsed.position_list
forces = parsed.force_list
gp_model = parsed.make_gp(kernel=two_plus_three_body_mc,
kernel_grad=two_plus_three_body_mc_grad)
# build up MGP from GP
struc_params = {
'species': [47, 53],
'cube_lat': np.eye(3) * 100,
'mass_dict': {
'0': 27,
'1': 16
}
}
# grid parameters
lower_cut = 2.5
grid_num_2 = 64
grid_num_3 = 32
two_cut = 5.0
three_cut = 5.0
grid_params = {
'bounds_2': [[lower_cut], [two_cut]],
'bounds_3': [[lower_cut, lower_cut, -1], [three_cut, three_cut, 1]],
'grid_num_2': grid_num_2,
'grid_num_3': [grid_num_3, grid_num_3, grid_num_3],
'svd_rank_2': 0,
'svd_rank_3': 0,
'bodies': [2, 3],
'load_grid': None,
'update': True
}
mgp_model = MappedGaussianProcess(gp_model.hyps,
gp_model.cutoffs,
grid_params,
struc_params,
mean_only=True,
container_only=False,
GP=gp_model,
lmp_file_name='lmp.mgp')
# ------------ create ASE's flare calculator -----------------------
flare_calc = FLARE_Calculator(gp_model,
mgp_model,
par=True,
use_mapping=True)
a = 3.855
alpha = 90
super_cell = crystal(
['Ag', 'I'], # Ag, I
basis=[(0, 0, 0), (0.5, 0.5, 0.5)],
size=(2, 1, 1),
cellpar=[a, a, a, alpha, alpha, alpha])
super_cell.positions = positions[-1]
super_cell.set_calculator(flare_calc)
super_cell.get_forces()
stresses = super_cell.calc.results['stresses']
# parse lammps stress
lmp_file = open('test_files/stress.lammps')
lines = lmp_file.readlines()[9:]
for ind, line in enumerate(lines):
line = line.split()
strs = np.array([float(l) for l in line[1:]]) / 1.60217662e6
assert np.isclose(stresses[ind], strs, rtol=1e-3).all()
os.system('rm -r __pycache__')
os.system('rm grid3*')
os.system('rm -r kv3')
os.system('rm lmp.mgp')
def test_lmp_predict(all_lmp, all_gp, all_mgp, bodies, multihyps):
"""
test the lammps implementation
"""
# pytest.skip()
prefix = f"{bodies}{multihyps}"
mgp_model = all_mgp[prefix]
gp_model = all_gp[prefix]
lmp_calculator = all_lmp[prefix]
ase_calculator = FLARE_Calculator(gp_model,
mgp_model,
par=False,
use_mapping=True)
# create test structure
np.random.seed(1)
cell = np.diag(np.array([1, 1, 1])) * 4
nenv = 10
unique_species = gp_model.training_statistics["species"]
cutoffs = gp_model.cutoffs
struc_test, f = get_random_structure(cell, unique_species, nenv)
# build ase atom from struc
ase_atoms_flare = struc_test.to_ase_atoms()
ase_atoms_flare = FLARE_Atoms.from_ase_atoms(ase_atoms_flare)
ase_atoms_flare.calc = ase_calculator
ase_atoms_lmp = deepcopy(struc_test).to_ase_atoms()
ase_atoms_lmp.calc = lmp_calculator
try:
lmp_en = ase_atoms_lmp.get_potential_energy()
flare_en = ase_atoms_flare.get_potential_energy()
lmp_stress = ase_atoms_lmp.get_stress()
flare_stress = ase_atoms_flare.get_stress()
lmp_forces = ase_atoms_lmp.get_forces()
flare_forces = ase_atoms_flare.get_forces()
except Exception as e:
os.chdir(curr_path)
print(e)
raise e
os.chdir(curr_path)
# check that lammps agrees with mgp to within 1 meV/A
print("energy", lmp_en - flare_en, flare_en)
assert np.isclose(lmp_en, flare_en, atol=1e-3)
print("force", lmp_forces - flare_forces, flare_forces)
assert np.isclose(lmp_forces, flare_forces, atol=1e-3).all()
print("stress", lmp_stress - flare_stress, flare_stress)
assert np.isclose(lmp_stress, flare_stress, atol=1e-3).all()
# check the lmp var
# mgp_std = np.sqrt(mgp_pred[1])
# print("isclose? diff:", lammps_stds[atom_num]-mgp_std, "mgp value", mgp_std)
# assert np.isclose(lammps_stds[atom_num], mgp_std, rtol=1e-2)
clean(prefix=prefix)
def flare_calc():
flare_calc_dict = {}
for md_engine in md_list:
# ---------- create gaussian process model -------------------
gp_model = GaussianProcess(
kernel_name='2+3_mc',
hyps=[0.1, 1., 0.001, 1, 0.06],
cutoffs=(5.0, 5.0),
hyp_labels=['sig2', 'ls2', 'sig3', 'ls3', 'noise'],
opt_algorithm='BFGS',
par=False)
# ----------- create mapped gaussian process ------------------
struc_params = {
'species': [1, 2],
'cube_lat': np.eye(3) * 100,
'mass_dict': {
'0': 2,
'1': 4
}
}
# grid parameters
lower_cut = 2.5
two_cut, three_cut = gp_model.cutoffs
grid_num_2 = 8
grid_num_3 = 8
grid_params = {
'bounds_2': [[lower_cut], [two_cut]],
'bounds_3': [[lower_cut, lower_cut, -1], [three_cut, three_cut,
1]],
'grid_num_2': grid_num_2,
'grid_num_3': [grid_num_3, grid_num_3, grid_num_3],
'svd_rank_2': 0,
'svd_rank_3': 0,
'bodies': [2, 3],
'load_grid': None,
'update': True
}
mgp_model = MappedGaussianProcess(gp_model.hyps,
gp_model.cutoffs,
grid_params,
struc_params,
mean_only=False,
container_only=False,
GP=gp_model,
lmp_file_name='lmp.mgp',
n_cpus=1)
# ------------ create ASE's flare calculator -----------------------
flare_calculator = FLARE_Calculator(gp_model,
mgp_model,
par=True,
use_mapping=True)
flare_calc_dict[md_engine] = flare_calculator
print(md_engine)
yield flare_calc_dict
del flare_calc_dict
