def calculate_energy(x0, trainer):
OH_bond_length = x0[0]
bond_angle = x0[1]
calculator = AMPtorch(trainer)
image = molecule('H2O')
image.set_distance(0, 2, OH_bond_length, fix=0)
image.set_angle(1, 0, 2, bond_angle)
image.set_cell([10, 10, 10])
image.center()
image.set_calculator(calculator)
return image.get_potential_energy()
# def optimize_energy(x0):
# OH_bond_length = x0[0]
# bond_angle = x0[1]
# trainer = AtomsTrainer()
# # loading the pretrained model
# trainer.load_pretrained("../checkpoints/2021-03-22-14-02-20-test")
# calculator = AMPtorch(trainer)
# image = molecule('H2O')
# image.set_distance(0, 2, OH_bond_length, fix=0)
# image.set_angle(1, 0, 2, bond_angle)
# image.set_cell([10, 10, 10])
# # fix the OH.
# fix_OH = FixAtoms([0, 1])
# image.set_constraint(fix_OH)
# image.center()
# image.set_calculator(calculator)
# print("Before minimizing forces: ")
# print(image.get_distance(0, 2))
# print(image.get_angle(1, 0, 2))
# print(image.get_potential_energy())
# dyn = BFGS(image)
# dyn.run(fmax=1e-3)
# print("Before minimizing forces: ")
# print(image.get_distance(0, 2))
# print(image.get_angle(1, 0, 2))
# return dyn
# optimize_energy([1.2, 120])
"scaling": {
"type": "normalize",
"range": (0, 1)
},
},
"cmd": {
"debug": False,
"run_dir": "./",
"seed": 1,
"identifier": "test",
"verbose": True,
# Weights and Biases used for logging - an account(free) is required
"logger": False,
},
}
torch.set_num_threads(1)
trainer = AtomsTrainer(config)
trainer.train()
predictions = trainer.predict(images)
true_energies = np.array([image.get_potential_energy() for image in images])
pred_energies = np.array(predictions["energy"])
print("Energy MSE:", np.mean((true_energies - pred_energies)**2))
print("Energy MAE:", np.mean(np.abs(true_energies - pred_energies)))
image.set_calculator(AMPtorch(trainer))
image.get_potential_energy()
def calculate_energy(x0):
OH_bond_length = x0[0]
bond_angle = x0[1]
images = read("../data/water.traj", index=":")
images = [images[0]]
sigmas = np.logspace(np.log10(0.02), np.log10(1.0), num=5)
MCSHs = {
"MCSHs": {
"0": {"groups": [1], "sigmas": sigmas},
"1": {"groups": [1], "sigmas": sigmas},
"2": {"groups": [1, 2], "sigmas": sigmas},
"3": {"groups": [1, 2, 3], "sigmas": sigmas},
"4": {"groups": [1, 2, 3, 4], "sigmas": sigmas},
"5": {"groups": [1, 2, 3, 4, 5], "sigmas": sigmas},
# "6": {"groups": [1, 2, 3, 4, 5, 6, 7], "sigmas": sigmas},
},
"atom_gaussians": {
"H": "../MCSH_potentials/H_pseudodensity_2.g",
"O": "../MCSH_potentials/O_pseudodensity_4.g",
},
"cutoff": 8,
}
elements = ["H", "O"]
config = {
"model": {"get_forces": True, "num_layers": 3, "num_nodes": 20},
"optim": {
"device": "cpu",
"force_coefficient": 0.2,
"lr": 1e-3,
"batch_size": 8,
"epochs": 500,
},
"dataset": {
"raw_data": images,
# "val_split": 0.1,
"elements": elements,
"fp_scheme": "mcsh",
"fp_params": MCSHs,
"save_fps": True,
},
"cmd": {
"debug": False,
"run_dir": "./",
"seed": 1,
"identifier": "test",
"verbose": True,
"logger": False,
},
}
trainer = AtomsTrainer(config)
# loading the pretrained model
trainer.load_pretrained("../checkpoints/2021-03-22-14-02-20-test")
calculator = AMPtorch(trainer)
image = molecule('H2O')
image.set_distance(0, 2, OH_bond_length, fix=0)
image.set_angle(1, 0, 2, bond_angle)
image.set_cell([10, 10, 10])
image.center()
image.set_calculator(calculator)
return image.get_potential_energy()
def get_calc(self):
return AMPtorch(self)
"verbose": True,
"logger": False,
},
}
config["dataset"]["cutoff_params"] = cosine_cutoff_params
torch.set_num_threads(1)
cosine_trainer = AtomsTrainer(config)
cosine_trainer.train()
predictions = cosine_trainer.predict(images)
true_energies = np.array([image.get_potential_energy() for image in images])
cosine_pred_energies = np.array(predictions["energy"])
image.set_calculator(AMPtorch(cosine_trainer))
image.get_potential_energy()
config["dataset"]["cutoff_params"] = polynomial_cutoff_params
torch.set_num_threads(1)
polynomial_trainer = AtomsTrainer(config)
polynomial_trainer.train()
predictions = polynomial_trainer.predict(images)
polynomial_pred_energies = np.array(predictions["energy"])
print("Energy MSE (Cosine):", np.mean(
(true_energies - cosine_pred_energies)**2))
print("Energy MAE (Cosine):",
np.mean(np.abs(true_energies - cosine_pred_energies)))