def setUpClass(cls) -> None:
# Set up parent calculator and image environment
initial_structure = ase.io.read(
"./relaxation_test_structures/Pt-NP.traj")
initial_structure.set_calculator(EMT())
# 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=0.05,
steps=100)
cls.EMT_structure_optim.run(cls.emt_counter, "PtNP_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=30,
maxstep=0.04)
cls.OAL_learner, cls.OAL_structure_optim = run_online_al(
OAL_relaxation, [OAL_initial_structure], "PtNP_oal", EMT())
# Retain images of the final structure from both relaxations
cls.EMT_image = cls.EMT_structure_optim.get_trajectory("PtNP_emt")[-1]
cls.EMT_image.set_calculator(EMT())
cls.OAL_image = cls.OAL_structure_optim.get_trajectory("PtNP_oal")[-1]
cls.OAL_image.set_calculator(EMT())
cls.description = "PtNP"
return super().setUpClass()
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=100, maxstep=0.04
)
cls.OAL_learner, cls.OAL_structure_optim = run_online_al(
OAL_relaxation,
[],
["Cu"],
"CuNP_oal",
CounterCalc(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 setUpClass(cls) -> None:
cls.emt_counter = CounterCalc(EMT())
ml2relax = True
cls.total_neb_images = 5
intermediate_images = 3
iter = 7
initial, final = construct_geometries(parent_calc=cls.emt_counter,
ml2relax=ml2relax)
images = [initial, final]
cls.EMT_neb = NEBcalc(images)
cls.EMT_neb.run(cls.emt_counter, filename="emt_neb")
initial1, final1 = construct_geometries(parent_calc=EMT(),
ml2relax=ml2relax)
images_neb = [initial1, final1]
cls.neb_AL_learner = offline_neb(images_neb, EMT(), iter,
intermediate_images)
cls.saddle_pt = int(cls.total_neb_images / 2)
cls.neb_AL_image = read("example_iter_" + str(iter) + ".traj@" +
str(-cls.saddle_pt))
cls.description = "CuC_NEB"
return super().setUpClass()