def check_if_atoms_interacting_energy(model, symbols, etol):
"""
First, get the energy of a single isolated atom of each species given in 'symbols'.
Then, construct a dimer consisting of these two species and try to decrease its bond
length until a discernible difference in the energy (from the sum of the isolated
energy of each species) is detected. The 'symbols' arg should be a list or tuple of
length 2 indicating which species pair to check, e.g. to check if Al interacts with
Al, one should specify ['Al', 'Al'].
"""
if not isinstance(symbols, (list, tuple)) or len(symbols) != 2:
raise ValueError(
"Argument 'symbols' passed to check_if_atoms_interacting_energy "
"must be a list of tuple of length 2 indicating the species pair to "
"check"
)
isolated_energy_per_atom = {}
isolated_energy_per_atom[symbols[0]] = get_isolated_energy_per_atom(
model, symbols[0]
)
isolated_energy_per_atom[symbols[1]] = get_isolated_energy_per_atom(
model, symbols[1]
)
dimer = Atoms(
symbols,
positions=[(0.1, 0.1, 0.1), (5.1, 0.1, 0.1)],
cell=(20, 20, 20),
pbc=(False, False, False),
)
calc = KIM(model)
dimer.set_calculator(calc)
try:
rescale_to_get_nonzero_energy(dimer, isolated_energy_per_atom, etol)
atoms_interacting = True
return atoms_interacting
except: # noqa: E722
atoms_interacting = False
return atoms_interacting
finally:
if hasattr(calc, "__del__"):
calc.__del__()
del dimer
from ase.lattice.cubic import FaceCenteredCubic
from ase.calculators.kim.kim import KIM
atoms = FaceCenteredCubic(symbol='Ar', latticeconstant=5.25, size=(1, 1, 1))
calc = KIM('LJ_ElliottAkerson_2015_Universal__MO_959249795837_003')
atoms.calc = calc
energy = atoms.get_potential_energy()
print('Potential energy: {} eV'.format(energy))
#!/usr/bin/env python3
from ase import Atom, Atoms
from ase.build import bulk, fcc100, add_adsorbate, add_vacuum
from ase.calculators.vasp import Vasp
from ase.calculators.kim.kim import KIM
from ase.calculators.qmmm import ForceQMMM, RescaledCalculator
from ase.constraints import StrainFilter
from ase.optimize import LBFGS
from ase.visualize import view
atoms = bulk("Pd", "fcc", a=3.5, cubic=True)
atoms.calc = KIM("MEAM_LAMMPS_JeongParkDo_2018_PdMo__MO_356501945107_000")
opt = LBFGS(StrainFilter(atoms), logfile=None)
opt.run(0.03, steps=30)
length = atoms.cell.cellpar()[0]
atoms = fcc100("Pd", (2,2,5), a=length, vacuum=10, periodic=True)
add_adsorbate(atoms, Atoms([Atom("Mo")]), 1.2)
qm_mask = [len(atoms)-1, len(atoms)-2]
qm_calc = Vasp(directory="./qmmm")
mm_calc = KIM("MEAM_LAMMPS_JeongParkDo_2018_PdMo__MO_356501945107_000")
mm_calc = RescaledCalculator(mm_calc, 1, 1, 1, 1)
qmmm = ForceQMMM(atoms, qm_mask, qm_calc, mm_calc, buffer_width=3)
qmmm.initialize_qm_buffer_mask(atoms)
atoms.pbc=True
atoms.calc = qmmm
print(atoms.get_forces())
def get_model_energy_cutoff(
model,
symbols,
xtol=1e-8,
etol_coarse=1e-6,
etol_fine=1e-15,
max_bisect_iters=1000,
max_upper_cutoff_bracket=20.0,
):
"""
Compute the distance at which energy interactions become non-trival for a given
model and a species pair it supports. This is done by constructing a dimer composed
of these species in a large finite box, increasing the separation if necessary until
the total potential energy is within 'etol_fine' of the sum of the corresponding
isolated energies, and then shrinking the separation until the energy differs from
that value by more than 'etol_coarse'. Using these two separations to bound the
search range, bisection is used to refine in order to locate the cutoff. The
'symbols' arg should be a list or tuple of length 2 indicating which species pair to
check, e.g. to get the energy cutoff of Al with Al, one should specify ['Al', 'Al'].
This function is based on the content of the DimerContinuityC1__VC_303890932454_002
Verification Check in OpenKIM [1-3].
[1] Tadmor E. Verification Check of Dimer C1 Continuity v002. OpenKIM; 2018.
doi:10.25950/43d2c6d5
[2] Tadmor EB, Elliott RS, Sethna JP, Miller RE, Becker CA. The potential of
atomistic simulations and the Knowledgebase of Interatomic Models. JOM.
2011;63(7):17. doi:10.1007/s11837-011-0102-6
[3] Elliott RS, Tadmor EB. Knowledgebase of Interatomic Models (KIM) Application
Programming Interface (API). OpenKIM; 2011. doi:10.25950/ff8f563a
"""
from scipy.optimize import bisect
def get_dimer_positions(a, large_cell_len):
"""
Generate positions for a dimer of length 'a' centered in a finite simulation box
with side length 'large_cell_len'
"""
half_cell = 0.5 * large_cell_len
positions = [
[half_cell - 0.5 * a, half_cell, half_cell],
[half_cell + 0.5 * a, half_cell, half_cell],
]
return positions
def energy(a, dimer, large_cell_len, einf):
dimer.set_positions(get_dimer_positions(a, large_cell_len))
return dimer.get_potential_energy() - einf
def energy_cheat(a, dimer, large_cell_len, offset, einf):
dimer.set_positions(get_dimer_positions(a, large_cell_len))
return (dimer.get_potential_energy() - einf) + offset
if not isinstance(symbols, (list, tuple)) or len(symbols) != 2:
raise ValueError(
"Argument 'symbols' passed to check_if_atoms_interacting_energy "
"must be a list of tuple of length 2 indicating the species pair to "
"check"
)
isolated_energy_per_atom = {}
isolated_energy_per_atom[symbols[0]] = get_isolated_energy_per_atom(
model, symbols[0]
)
isolated_energy_per_atom[symbols[1]] = get_isolated_energy_per_atom(
model, symbols[1]
)
einf = isolated_energy_per_atom[symbols[0]] + isolated_energy_per_atom[symbols[1]]
# First, establish the upper bracket cutoff by starting at 'b_init' Angstroms and
# incrementing by 'db' until
b_init = 4.0
# Create finite box of size large_cell_len
large_cell_len = 50
dimer = Atoms(
symbols,
positions=get_dimer_positions(b_init, large_cell_len),
cell=(large_cell_len, large_cell_len, large_cell_len),
pbc=(False, False, False),
)
calc = KIM(model)
dimer.set_calculator(calc)
db = 2.0
b = b_init - db
still_interacting = True
while still_interacting:
b += db
if b > max_upper_cutoff_bracket:
if hasattr(calc, "__del__"):
calc.__del__()
raise KIMASEError(
"Exceeded limit on upper bracket when determining cutoff "
"search range"
)
else:
eb = energy(b, dimer, large_cell_len, einf)
if abs(eb) < etol_fine:
still_interacting = False
a = b
da = 0.01
not_interacting = True
while not_interacting:
a -= da
if a < 0:
if hasattr(calc, "__del__"):
calc.__del__()
raise RuntimeError(
"Failed to determine lower bracket for cutoff search using etol_coarse "
"= {}. This may mean that the species pair provided ({}) does not "
"have a non-trivial energy interaction for the potential being "
"used.".format(etol_coarse, symbols)
)
else:
ea = energy(a, dimer, large_cell_len, einf)
if abs(ea) > etol_coarse:
not_interacting = False
# NOTE: Some Simulator Models have a history dependence due to them maintaining
# charges from the previous energy evaluation to use as an initial guess
# for the next charge equilibration. We therefore have to treat them not
# as single-valued functions but as distributions, i.e. for a given
# configuration you might get any of a range of energy values depending on
# the history of your previous energy evaluations. This is particularly
# problematic for this step, where we set up a bisection problem in order
# to determine the cutoff radius of the model. Our solution for this
# specific case is to make a very crude estimate of the variance of that
# distribution with a 10% factor of safety on it.
eb_new = energy(b, dimer, large_cell_len, einf)
eb_error = abs(eb_new - eb)
# compute offset to ensure that energy before and after cutoff have
# different signs
if ea < eb:
offset = -eb + 1.1 * eb_error + np.finfo(float).eps
else:
offset = -eb - 1.1 * eb_error - np.finfo(float).eps
rcut, results = bisect(
energy_cheat,
a,
b,
args=(dimer, large_cell_len, offset, einf),
full_output=True,
xtol=xtol,
maxiter=max_bisect_iters,
)
# General clean-up
if hasattr(calc, "__del__"):
calc.__del__()
if not results.converged:
raise RuntimeError(
"Bisection search to find cutoff distance did not converge "
"within {} iterations with xtol = {}".format(max_bisect_iters, xtol)
)
else:
return rcut
def run_md(atoms, id, settings_fn):
"""The function does Molecular Dyanamic simulation (MD) on a material, given by argument atoms.
Parameters:
atoms (obj): an atoms object defined by class in ase. This is the material which MD
will run on.
id (int): an identifying number for the material.
Returns:
obj:atoms object defined in ase, is returned.
"""
# Read settings
settings = read_settings_file(settings_fn)
initial_unitcell_atoms = copy.deepcopy(atoms)
# Scale atoms object, cubic
size = settings['supercell_size']
atoms = atoms * size * (1, 1, 1)
# atoms = atoms * (size,size,size)
#print(atoms.get_chemical_symbols())
N = len(atoms.get_chemical_symbols())
# Use KIM for potentials from OpenKIM
use_kim = settings['use_kim']
# Use Asap for a huge performance increase if it is installed
use_asap = True
# Create a copy of the initial atoms object for future reference
old_atoms = copy.deepcopy(atoms)
# Describe the interatomic interactions with OpenKIM potential
if use_kim: # use KIM potential
atoms.calc = KIM(
"LJ_ElliottAkerson_2015_Universal__MO_959249795837_003")
else: # otherwise, default to asap3 LennardJones
atoms.calc = LennardJones([18], [0.010323], [3.40],
rCut=6.625,
modified=True)
# Set the momenta corresponding to temperature from settings file
MaxwellBoltzmannDistribution(atoms, settings['temperature'] * units.kB)
# Select integrator
if settings['ensemble'] == "NVE":
from ase.md.verlet import VelocityVerlet
dyn = VelocityVerlet(atoms, settings['time_step'] * units.fs)
elif settings['ensemble'] == "NVT":
from ase.md.langevin import Langevin
dyn = Langevin(atoms, settings['time_step'] * units.fs,
settings['temperature'] * units.kB,
settings['friction'])
interval = settings['interval']
# Creates trajectory files in directory trajectory_files
traj = Trajectory("trajectory_files/" + id + ".traj", 'w', atoms)
dyn.attach(traj.write, interval=interval)
# Number of decimals for most calculated properties.
decimals = settings['decimals']
# Boolean indicating if the material is monoatomic.
monoatomic = len(set(atoms.get_chemical_symbols())) == 1
# Calculation and writing of properties
properties.initialize_properties_file(atoms, initial_unitcell_atoms, id,
decimals, monoatomic)
dyn.attach(properties.calc_properties, 100, old_atoms, atoms, id, decimals,
monoatomic)
# unnecessary, used for logging md runs
# we should write some kind of logger for the MD
def logger(a=atoms): # store a reference to atoms in the definition.
"""Function to print the potential, kinetic and total energy."""
epot = a.get_potential_energy() / len(a)
ekin = a.get_kinetic_energy() / len(a)
t = ekin / (1.5 * units.kB)
print('Energy per atom: Epot = %.3feV Ekin = %.3feV (T=%3.0fK) '
'Etot = %.3feV' % (epot, ekin, t, epot + ekin))
# Running the dynamics
dyn.attach(logger, interval=interval)
#logger()
#dyn.run(settings['max_steps'])
# check for thermal equilibrium
counter = 0
equilibrium = False
for i in range(round(settings['max_steps'] /
settings['search_interval'])): # hyperparameter
epot, ekin_pre, etot, t = properties.energies_and_temp(atoms)
# kör steg som motsvarar säg 5 fs
dyn.run(settings['search_interval']) # hyperparamter
epot, ekin_post, etot, t = properties.energies_and_temp(atoms)
#print(abs(ekin_pre-ekin_post) / math.sqrt(N))
#print(counter)
if (abs(ekin_pre - ekin_post) / math.sqrt(N)) < settings['tolerance']:
counter += 1
else:
counter = 0
if counter > settings['threshold']: # hyperparameter
print("reached equilibrium")
equilibrium = True
break
if equilibrium:
dyn.run(settings['max_steps'])
properties.finalize_properties_file(atoms, id, decimals, monoatomic)
else:
properties.delete_properties_file(id)
raise RuntimeError("MD did not find equilibrium")
return atoms
def run_md(atoms, id):
# Read settings
settings = read_settings_file()
# Use KIM for potentials from OpenKIM
use_kim = True
# Use Asap for a huge performance increase if it is installed
use_asap = True
# Create a copy of the initial atoms object for future reference
old_atoms = copy.deepcopy(atoms)
# Describe the interatomic interactions with OpenKIM potential
if use_kim: # use KIM potential
atoms.calc = KIM(
"LJ_ElliottAkerson_2015_Universal__MO_959249795837_003")
else: # otherwise, default to asap3 LennardJones
atoms.calc = LennardJones([18], [0.010323], [3.40],
rCut=6.625,
modified=True)
# Set the momenta corresponding to temperature from settings file
MaxwellBoltzmannDistribution(atoms, settings['temperature'] * units.kB)
# Select integrator
if settings['ensemble'] == "NVE":
from ase.md.verlet import VelocityVerlet
dyn = VelocityVerlet(atoms, settings['time_step'] * units.fs)
elif settings['ensemble'] == "NVT":
from ase.md.langevin import Langevin
dyn = Langevin(atoms, settings['time_step'] * units.fs,
settings['temperature'] * units.kB,
settings['friction'])
traj = Trajectory('ar.traj', 'w', atoms)
dyn.attach(traj.write, interval=1000)
# Identity number given as func. parameter to keep track of properties
# Number of decimals for most calculated properties
decimals = settings['decimals']
# Calculation and writing of properties
properties.initialize_properties_file(atoms, id, decimals)
dyn.attach(properties.calc_properties, 100, old_atoms, atoms, id, decimals)
# unnecessary, used for logging md runs
# we should write some kind of logger for the MD
def logger(a=atoms): # store a reference to atoms in the definition.
"""Function to print the potential, kinetic and total energy."""
epot = a.get_potential_energy() / len(a)
ekin = a.get_kinetic_energy() / len(a)
t = ekin / (1.5 * units.kB)
print('Energy per atom: Epot = %.3feV Ekin = %.3feV (T=%3.0fK) '
'Etot = %.3feV' % (epot, ekin, t, epot + ekin))
# Running the dynamics
dyn.attach(logger, interval=1000)
logger()
dyn.run(settings['max_steps'])
return atoms