"cmd": {
"debug": False,
"run_dir": "./",
"seed": 1,
"identifier": "test",
"verbose": True,
"logger": False,
},
}
trainer = AtomsTrainer(config)
# building base morse calculator as base calculator
cutoff = Gs["default"]["cutoff"]
base_calc = MultiMorse(images, cutoff, combo="mean")
# define learner_params OfflineActiveLearner
learner_params = {
"atomistic_method":
Relaxation(initial_geometry=slab.copy(),
optimizer=BFGS,
fmax=0.01,
steps=100),
"max_iterations":
10,
"samples_to_retrain":
2,
"filename":
"example",
def run_delta_al(atomistic_method, images, elements, dbname, parent_calc):
Gs = {
"default": {
"G2": {
"etas": np.logspace(np.log10(0.05), np.log10(5.0), num=4),
"rs_s": [0],
},
"G4": {"etas": [0.005], "zetas": [1.0, 4.0], "gammas": [1.0, -1.0]},
"cutoff": 6,
},
}
learner_params = {
"max_iterations": 10,
"samples_to_retrain": 1,
"filename": "relax_example",
"file_dir": "./",
"stat_uncertain_tol": 0.15,
"dyn_uncertain_tol": 1.5,
"fmax_verify_threshold": 0.05, # eV/AA
"relative_variance": True,
"n_ensembles": 5,
"use_dask": True,
}
config = {
"model": {"get_forces": True, "num_layers": 3, "num_nodes": 5},
"optim": {
"device": "cpu",
"force_coefficient": 4.0,
"lr": 1e-2,
"batch_size": 10,
"epochs": 100,
"optimizer": torch.optim.LBFGS,
"optimizer_args": {"optimizer__line_search_fn": "strong_wolfe"},
},
"dataset": {
"raw_data": images,
"val_split": 0,
"elements": elements,
"fp_params": Gs,
"save_fps": False,
"scaling": {"type": "standardize"},
},
"cmd": {
"debug": False,
"run_dir": "./",
"seed": 1,
"identifier": "test",
"verbose": False,
# "logger": True,
"single-threaded": True,
},
}
trainer = AtomsTrainer(config)
base_calc = MultiMorse(images=images, cutoff=Gs["default"]["cutoff"])
ml_potential = AmptorchEnsembleCalc(trainer, learner_params["n_ensembles"])
deltacalc = DeltaLearner(
learner_params,
images,
ml_potential,
parent_calc,
base_calc,
)
if os.path.exists("dft_calls.db"):
os.remove("dft_calls.db")
atomistic_method.run(deltacalc, filename=dbname, replay_traj=True)
return deltacalc, atomistic_method
def run_offline_al(atomistic_method, images, dbname, parent_calc):
Gs = {
"default": {
"G2": {
"etas": np.logspace(np.log10(0.05), np.log10(5.0), num=4),
"rs_s": [0] * 4,
},
"G4": {"etas": [0.005], "zetas": [1.0, 4.0], "gammas": [1.0, -1.0]},
"cutoff": 6,
},
}
elements = np.unique(images[0].get_chemical_symbols())
learner_params = {
"atomistic_method": atomistic_method,
"max_iterations": 10,
"force_tolerance": 0.01,
"samples_to_retrain": 2,
"filename": "relax_example",
"file_dir": "./",
"query_method": "random",
"use_dask": False,
"max_evA": 0.05,
}
config = {
"model": {
"get_forces": True,
"num_layers": 3,
"num_nodes": 20,
},
"optim": {
"device": "cpu",
"force_coefficient": 30,
"lr": 1,
"batch_size": 10,
"epochs": 100, # was 100
"loss": "mse",
"metric": "mae",
"optimizer": torch.optim.LBFGS,
"optimizer_args": {"optimizer__line_search_fn": "strong_wolfe"},
},
"dataset": {
"raw_data": images,
"val_split": 0,
"elements": elements,
"fp_params": Gs,
"save_fps": False,
"scaling": {"type": "normalize", "range": (-1, 1)},
},
"cmd": {
"debug": False,
"run_dir": "./",
"seed": 1,
"identifier": "test",
"verbose": True,
# "logger": True,
"single-threaded": True,
},
}
trainer = AtomsTrainer(config)
cutoff = Gs["default"]["cutoff"]
base_calc = MultiMorse(images, cutoff, combo="mean")
learner = FmaxLearner(
learner_params,
images,
trainer,
parent_calc,
base_calc,
)
learner.learn()
trained_calc = learner.trained_calc
al_iterations = learner.iterations - 1
file_path = learner_params["file_dir"] + learner_params["filename"]
final_ml_traj = ase.io.read("{}_iter_{}.traj".format(file_path, al_iterations), ":")
if os.path.exists("dft_calls.db"):
os.remove("dft_calls.db")
# atomistic_method.run(learner, filename=dbname)
return learner, trained_calc, final_ml_traj
def offline_neb(images, parent_calc, iter=4, intermediate_images=3):
torch.set_num_threads(1)
parent_calc = parent_calc
Gs = {
"default": {
"G2": {
"etas": np.logspace(np.log10(0.05), np.log10(5.0), num=4),
"rs_s": [0],
},
"G4": {
"etas": [0.005],
"zetas": [1.0, 4.0],
"gammas": [1.0, -1.0]
},
"cutoff": 5.0,
},
}
elements = ["Cu", "C"]
config = {
"model": {
"get_forces": True,
"num_layers": 3,
"num_nodes": 20,
"activation": Tanh,
},
"optim": {
"device": "cpu",
"force_coefficient": 27,
"lr": 1e-2,
"batch_size": 1000,
"epochs": 300,
"loss": "mse",
"metric": "mse",
"optimizer": torch.optim.LBFGS,
"optimizer_args": {
"optimizer__line_search_fn": "strong_wolfe"
},
"scheduler": {
"policy": torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
"params": {
"T_0": 10,
"T_mult": 2
},
},
},
"dataset": {
"raw_data": images,
"val_split": 0,
"elements": elements,
"fp_params": Gs,
"save_fps": True,
"scaling": {
"type": "normalize",
"range": (-1, 1)
},
},
"cmd": {
"debug": False,
"run_dir": "./",
"seed": 1,
"identifier": "test",
"verbose": True,
"logger": False,
"dtype": torch.DoubleTensor,
},
}
trainer = AtomsTrainer(config)
# building base morse calculator as base calculator
cutoff = Gs["default"]["cutoff"]
neb_images = images.copy()
base_calc = MultiMorse(neb_images, cutoff, combo="mean")
# base_calc = Dummy(images)
# define learner_params OfflineActiveLearner
learner_params = {
"atomistic_method":
NEBcalc(
starting_images=neb_images,
intermediate_samples=intermediate_images,
),
"max_iterations":
iter,
"samples_to_retrain":
intermediate_images,
"filename":
"example",
"file_dir":
"./",
"use_dask":
False,
# "max_evA": 0.01,
}
learner = NEBLearner(learner_params, images, trainer, parent_calc,
base_calc)
learner.learn()
return learner
