def setUpClass(cls) -> None:
initial_structure = Icosahedron("Cu", 2)
initial_structure.rattle(0.1)
initial_structure.set_pbc(True)
initial_structure.set_cell([20, 20, 20])
EMT_initial_structure = initial_structure.copy()
parent_calc = EMT()
cls.emt_counter = CounterCalc(parent_calc)
EMT_initial_structure.set_calculator(cls.emt_counter)
cls.EMT_structure_optim = Relaxation(
EMT_initial_structure, BFGS, fmax=0.01, steps=30
)
cls.EMT_structure_optim.run(cls.emt_counter, "CuNP_emt")
offline_initial_structure = compute_with_calc(
[initial_structure.copy()], parent_calc
)[0]
Offline_relaxation = Relaxation(
offline_initial_structure, BFGS, fmax=0.01, steps=30, maxstep=0.05
)
cls.offline_learner, cls.trained_calc, cls.Offline_traj = run_offline_al(
Offline_relaxation,
[offline_initial_structure],
"CuNP_offline_al",
parent_calc,
)
cls.EMT_image = cls.EMT_structure_optim.get_trajectory("CuNP_emt")[-1]
cls.EMT_image.set_calculator(parent_calc)
cls.offline_final_structure_AL = cls.Offline_traj[-1]
cls.offline_final_structure_AL.set_calculator(cls.trained_calc)
cls.offline_final_structure_EMT = cls.Offline_traj[-1]
cls.offline_final_structure_EMT.set_calculator(parent_calc)
cls.description = "CuNP"
return super().setUpClass()
def setUpClass(cls) -> None:
# Set up parent calculator and image environment
initial_structure = Icosahedron("Cu", 2)
initial_structure.rattle(0.1)
initial_structure.set_pbc(True)
initial_structure.set_cell([20, 20, 20])
# Run relaxation with the parent calc
EMT_initial_structure = initial_structure.copy()
cls.emt_counter = CounterCalc(EMT())
EMT_initial_structure.set_calculator(cls.emt_counter)
cls.EMT_structure_optim = Relaxation(EMT_initial_structure,
BFGS,
fmax=FORCE_THRESHOLD,
steps=30)
cls.EMT_structure_optim.run(cls.emt_counter, "CuNP_emt")
# Run relaxation with active learning
chemical_formula = initial_structure.get_chemical_formula()
al_config = cls.get_al_config()
al_config["links"]["traj"] = "CuNP_emt.traj"
cls.oal_results_dict = active_learning(al_config)
dbname = (str(al_config["links"]["ml_potential"]) + "_" +
str(chemical_formula) + "_oal")
cls.OAL_image = Trajectory(dbname + ".traj")[-1]
cls.OAL_image.set_calculator(EMT())
# Retain images of the final structure from both relaxations
cls.EMT_image = cls.EMT_structure_optim.get_trajectory("CuNP_emt")[-1]
cls.EMT_image.set_calculator(EMT())
cls.description = "CuNP"
return super().setUpClass()
def setUpClass(cls) -> None:
# Set up parent calculator and image environment
initial_structure = Icosahedron("Cu", 2)
initial_structure.rattle(0.1)
initial_structure.set_pbc(True)
initial_structure.set_cell([20, 20, 20])
# Run relaxation with the parent calc
EMT_initial_structure = initial_structure.copy()
cls.emt_counter = CounterCalc(EMT())
EMT_initial_structure.set_calculator(cls.emt_counter)
cls.EMT_structure_optim = Relaxation(EMT_initial_structure,
BFGS,
fmax=FORCE_THRESHOLD,
steps=30)
cls.EMT_structure_optim.run(cls.emt_counter, "CuNP_emt")
# Run relaxation with active learning
OAL_initial_structure = initial_structure.copy()
OAL_initial_structure.set_calculator(EMT())
OAL_relaxation = Relaxation(OAL_initial_structure,
BFGS,
fmax=0.05,
steps=60,
maxstep=0.04)
cls.OAL_learner, cls.OAL_structure_optim = run_online_al(
OAL_relaxation,
[OAL_initial_structure],
["Cu"],
"CuNP_oal",
EMT(),
)
# Retain images of the final structure from both relaxations
cls.EMT_image = cls.EMT_structure_optim.get_trajectory("CuNP_emt")[-1]
cls.EMT_image.set_calculator(EMT())
cls.OAL_image = cls.OAL_structure_optim.get_trajectory("CuNP_oal")[-1]
cls.OAL_image.set_calculator(EMT())
cls.description = "CuNP"
return super().setUpClass()
def test_fake_ase():
# pysisyphus
pysis_calc = LennardJones()
ase_calc = FakeASE(pysis_calc)
# ASE atoms, pysisyphus calculator
atoms_pysis = Icosahedron("Ar", noshells=2, latticeconstant=3)
# ASE atoms, ASE calculator
atoms_ase = atoms_pysis.copy()
atoms_pysis.set_calculator(ase_calc)
atoms_ase.set_calculator(ase_LJ())
pysis_forces = atoms_pysis.get_forces()
ase_forces = atoms_ase.get_forces()
np.testing.assert_allclose(pysis_forces, ase_forces, atol=1e-15)
cluster = LocalCluster(processes=True, threads_per_worker=1)
client = Client(cluster)
AmptorchEnsembleCalc.set_executor(client)
# Set up parent calculator and image environment
initial_structure = Icosahedron("Cu", 2)
initial_structure.rattle(0.1)
initial_structure.set_pbc(True)
initial_structure.set_cell([20, 20, 20])
images = []
elements = ["Cu"]
parent_calc = EMT()
# Run relaxation with active learning
OAL_initial_structure = initial_structure.copy()
OAL_initial_structure.set_calculator(copy.deepcopy(parent_calc))
OAL_relaxation = Relaxation(OAL_initial_structure,
BFGS,
fmax=0.05,
steps=200,
maxstep=0.04)
Gs = {
"default": {
"G2": {
"etas": np.logspace(np.log10(0.05), np.log10(5.0), num=4),
"rs_s": [0],
},
"G4": {
"etas": [0.005],
cluster = LocalCluster(processes=True, threads_per_worker=1)
client = Client(cluster)
EnsembleCalc.set_executor(client)
# Set up parent calculator and image environment
initial_structure = Icosahedron("Cu", 2)
initial_structure.rattle(0.1)
initial_structure.set_pbc(True)
initial_structure.set_cell([20, 20, 20])
images = []
elements = ["Cu"]
parent_calc = EMT()
# Run relaxation with active learning
OAL_initial_structure = initial_structure.copy()
OAL_initial_structure.set_calculator(copy.deepcopy(parent_calc))
OAL_relaxation = Relaxation(OAL_initial_structure,
BFGS,
fmax=0.05,
steps=60,
maxstep=0.04)
Gs = {
"default": {
"G2": {
"etas": np.logspace(np.log10(0.05), np.log10(5.0), num=4),
"rs_s": [0],
},
"G4": {
"etas": [0.005],