optimizers

Hint

Modified ase optimizers by implementing Torch tensor calculation.

Examples of using optimizers:

###############################################################################
# Detect GPU card
###############################################################################
import torch
if torch.cuda.is_available():
    device='cuda'
    print("CUDA is available.")
    print(f"Number of GPUs: {torch.cuda.device_count()}")
    print(f"GPU Name: {torch.cuda.get_device_name(0)}")
else:
    device='cpu'
    print("CUDA is NOT available.")

import dgl
u, v = torch.tensor([0, 0, 0, 1], device=device), torch.tensor([1, 2, 3, 3],
                                                               device=device)
g = dgl.graph((u, v), device=device)
print(f'DGL graph device: {g.device}')

###############################################################################
# Optimizers
###############################################################################
import os
from ase.io import read, write
from ase import Atoms
from agat.app.optimizers import BFGSTorch, MDMinTorch
from agat.app.calculators import AgatCalculator
# --- 1 --- BFGSTorch
model_save_dir = os.path.join('potential_models', 'agat_model_1')
graph_build_scheme_dir = os.path.join('potential_models')
atoms = read(os.path.join('potential_models', 'POSCAR'))
calculator=AgatCalculator(model_save_dir,
                          graph_build_scheme_dir,
                          device=device)
atoms = Atoms(atoms, calculator=calculator)
dyn = BFGSTorch(atoms, trajectory='test.traj', device=device)
dyn.run(fmax=1.0)
traj = read('test.traj', index=':')
write("XDATCAR.gat", traj)

# --- 2 --- MDMinTorch
model_save_dir = os.path.join('potential_models', 'agat_model_1')
graph_build_scheme_dir = os.path.join('potential_models')
atoms = read(os.path.join('potential_models', 'POSCAR'))
calculator=AgatCalculator(model_save_dir,
                          graph_build_scheme_dir,
                          device=device)
atoms = Atoms(atoms, calculator=calculator)
dyn = MDMinTorch(atoms, trajectory='test.traj', device=device)
dyn.run(fmax=1.0)

# --- 3 --- ase.optimize.BFGS
from ase.optimize import BFGS
model_save_dir = os.path.join('potential_models', 'agat_model_1')
graph_build_scheme_dir = os.path.join('potential_models')
atoms = read(os.path.join('potential_models', 'POSCAR'))
calculator=AgatCalculator(model_save_dir,
                          graph_build_scheme_dir,
                          device=device)
atoms = Atoms(atoms, calculator=calculator)
dyn = BFGS(atoms, trajectory='test.traj')
dyn.run(fmax=1.0)

BFGSTorch

__init__(self, atoms: Atoms, restart: str | None = None, logfile: IO | str | None = '-', trajectory: str | None = None, append_trajectory: bool = False, maxstep: float | None = None, master: bool | None = None, alpha: float | None = None, device=torch.device('cuda'))

Parameters:

device (str/torch.device) –

Device to run the simulation.

restart: string

Pickle file used to store hessian matrix. If set, file with such a name will be searched and hessian matrix stored will be used, if the file exists.

trajectory: string

Pickle file used to store trajectory of atomic movement.

logfile: file object or str

If logfile is a string, a file with that name will be opened. Use ‘-’ for stdout.

maxstep: float

Used to set the maximum distance an atom can move per iteration (default value is 0.2 Å).

master: boolean

Defaults to None, which causes only rank 0 to save files. If set to true, this rank will save files.

alpha: float

Initial guess for the Hessian (curvature of energy surface). A conservative value of 70.0 is the default, but number of needed steps to converge might be less if a lower value is used. However, a lower value also means risk of instability.

MDMinTorch

__init__(self, atoms: Atoms, restart: Optional[str] = None, logfile: Union[IO, str] = '-', trajectory: Optional[str] = None, dt: Optional[float] = None, maxstep: Optional[float] = None, master: Optional[bool] = None, 'cuda')

Parameters:

device (str/torch.device) –

Device to run the simulation.

restart: string

Pickle file used to store hessian matrix. If set, file with such a name will be searched and hessian matrix stored will be used, if the file exists.

trajectory: string

Pickle file used to store trajectory of atomic movement.

logfile: string

Text file used to write summary information.

dt: float

Time step for integrating the equation of motion.

maxstep: float

Spatial step limit in Angstrom. This allows larger values of dt while being more robust to instabilities in the optimization.

master: boolean

Defaults to None, which causes only rank 0 to save files. If set to true, this rank will save files.