def __init__(
self,
atoms,
timestep,
temperature,
nvt_q,
trajectory=None,
logfile=None,
loginterval=1,
append_trajectory=False,
):
# set angular and com momentum to zero, necessary for nose-hoover dynamics.
ZeroRotation(atoms)
Stationary(atoms)
# thermostat parameters
self.temp = temperature / units.kB
self.nvt_q = nvt_q
self.dt = timestep
self.dtdt = np.power(self.dt, 2)
self.nvt_bath = 0.0
self.natoms = len(atoms)
MolecularDynamics.__init__(
self,
atoms,
timestep,
trajectory,
logfile,
loginterval,
append_trajectory=append_trajectory,
)
def init_vel(cml):
'''
'''
arg = parse_cml_args(cml)
# read in the initial structure
init_pos = read(arg.poscar)
# set the momenta corresponding to T
MaxwellBoltzmannDistribution(init_pos, arg.temperature * ase.units.kB)
Stationary(init_pos)
ZeroRotation(init_pos)
# scale the temperature to T
vel = init_pos.get_velocities()
Tn = init_pos.get_temperature()
vel *= np.sqrt(arg.temperature / Tn)
init_pos.set_velocities(vel)
# write the structure
write(arg.out, init_pos, vasp5=True, direct=True)
# units in VASP and ASE are different
vel = init_pos.get_velocities() * ase.units.fs
# np.savetxt('init_vel.dat', vel, fmt='%20.16f')
# append the velocities to the POSCAR
with open(arg.out, 'a+') as pos:
pos.write('\n')
pos.write(
'\n'.join([
''.join(["%20.16f" % x for x in row])
for row in vel
])
)
def test_otf_md(md_engine, md_params, super_cell, flare_calc, qe_calc):
np.random.seed(12345)
flare_calculator = flare_calc[md_engine]
# set up OTF MD engine
otf_params = {
'init_atoms': [0, 1, 2, 3],
'output_name': md_engine,
'std_tolerance_factor': 2,
'max_atoms_added': len(super_cell.positions),
'freeze_hyps': 10
}
# 'use_mapping': flare_calculator.use_mapping}
md_kwargs = md_params[md_engine]
# intialize velocity
temperature = md_params['temperature']
MaxwellBoltzmannDistribution(super_cell, temperature * units.kB)
Stationary(super_cell) # zero linear momentum
ZeroRotation(super_cell) # zero angular momentum
super_cell.set_calculator(flare_calculator)
test_otf = ASE_OTF(super_cell,
timestep=1 * units.fs,
number_of_steps=3,
dft_calc=qe_calc,
md_engine=md_engine,
md_kwargs=md_kwargs,
**otf_params)
# TODO: test if mgp matches gp
# TODO: see if there's difference between MD timestep & OTF timestep
# set up logger
# otf_logger = OTFLogger(test_otf, super_cell,
# logfile=md_engine+'.log', mode="w", data_in_logfile=True)
# test_otf.attach(otf_logger, interval=1)
test_otf.run()
for f in glob.glob("scf*.pw*"):
os.remove(f)
for f in glob.glob("*.npy"):
os.remove(f)
for f in glob.glob("kv3*"):
shutil.rmtree(f)
for f in glob.glob("otf_data"):
shutil.rmtree(f, ignore_errors=True)
for f in glob.glob("out"):
shutil.rmtree(f, ignore_errors=True)
for f in os.listdir("./"):
if md_engine in f or 'lmp.mgp' in f:
os.remove(f)
if 'slurm' in f:
os.remove(f)
def otf_md_test(md_engine):
import atom_setup, flare_setup, qe_setup
np.random.seed(12345)
print(md_engine)
# ----------- set up atoms -----------------
super_cell = atom_setup.super_cell
# ----------- setup flare calculator ---------------
flare_calc = deepcopy(flare_setup.flare_calc)
super_cell.set_calculator(flare_calc)
# ----------- setup qe calculator --------------
dft_calc = qe_setup.dft_calc
# ----------- create otf object -----------
# set up OTF MD engine
md_params = {'timestep': 1 * units.fs, 'trajectory': None, 'dt': None,
'externalstress': 0, 'ttime': 25, 'pfactor': 3375,
'mask': None, 'temperature': 500, 'taut': 1, 'taup': 1,
'pressure': 0, 'compressibility': 0, 'fixcm': 1,
'friction': 0.02}
otf_params = {'dft_calc': dft_calc,
'init_atoms': [0, 1, 2, 3],
'std_tolerance_factor': 2,
'max_atoms_added' : len(super_cell.positions),
'freeze_hyps': 10,
'use_mapping': super_cell.calc.use_mapping}
# intialize velocity
temperature = md_params['temperature']
MaxwellBoltzmannDistribution(super_cell, temperature * units.kB)
Stationary(super_cell) # zero linear momentum
ZeroRotation(super_cell) # zero angular momentum
test_otf = otf_md(md_engine, super_cell, md_params, otf_params)
# set up logger
test_otf.attach(OTFLogger(test_otf, super_cell,
logfile=md_engine+'.log', mode="w", data_in_logfile=True),
interval=1)
# run otf
number_of_steps = 3
test_otf.otf_run(number_of_steps)
os.system('rm {}.log'.format(md_engine))
os.system('rm AgI.pw*')
os.system('rm -r out')
os.system('rm -r __pycache__')
os.system('rm -r kv3')
os.system('rm lmp.mgp')
os.system('rm -r otf_data')
os.system('rm *.npy')
def test_otf_md(md_engine, md_params, super_cell, flare_calc, qe_calc):
np.random.seed(12345)
flare_calculator = flare_calc[md_engine]
# set up OTF MD engine
otf_params = {
"init_atoms": [0, 1, 2, 3],
"output_name": md_engine,
"std_tolerance_factor": 2,
"max_atoms_added": len(super_cell.positions),
"freeze_hyps": 10,
"write_model": 1,
}
# 'use_mapping': flare_calculator.use_mapping}
md_kwargs = md_params[md_engine]
# intialize velocity
temperature = md_params["temperature"]
MaxwellBoltzmannDistribution(super_cell, temperature * units.kB)
Stationary(super_cell) # zero linear momentum
ZeroRotation(super_cell) # zero angular momentum
super_cell.set_calculator(flare_calculator)
test_otf = ASE_OTF(
super_cell,
timestep=1 * units.fs,
number_of_steps=number_of_steps,
dft_calc=qe_calc,
md_engine=md_engine,
md_kwargs=md_kwargs,
trajectory=md_engine + "_otf.traj",
**otf_params,
)
# TODO: test if mgp matches gp
# TODO: see if there's difference between MD timestep & OTF timestep
test_otf.run()
for f in glob.glob("scf*.pw*"):
os.remove(f)
for f in glob.glob("*.npy"):
os.remove(f)
for f in glob.glob("kv3*"):
shutil.rmtree(f)
for f in glob.glob("otf_data"):
shutil.rmtree(f, ignore_errors=True)
for f in glob.glob("out"):
shutil.rmtree(f, ignore_errors=True)
for f in os.listdir("./"):
if ".mgp" in f or ".var" in f:
os.remove(f)
if "slurm" in f:
os.remove(f)
def build_atoms(self, system, size, temp, seed=None):
assert system in self.systems.keys(), "System {} not found!".format(
system)
atoms = self.systems[system](size)
rng = np.random
if seed is not None:
rng.seed(seed)
MaxwellBoltzmannDistribution(atoms, temp * units.kB, rng=rng)
Stationary(atoms)
ZeroRotation(atoms)
return atoms
def _init_velocities(self, temp_init=300, remove_translation=True, remove_rotation=True):
"""
Initialize velocities for molecular dynamics
Args:
temp_init (float): Initial temperature in Kelvin (default 300)
remove_translation (bool): Remove translation components of velocity (default True)
remove_rotation (bool): Remove rotation components of velocity (default True)
"""
MaxwellBoltzmannDistribution(self.molecule, temp_init * units.kB)
if remove_translation:
Stationary(self.molecule)
if remove_rotation:
ZeroRotation(self.molecule)
def init_velocities(atoms, temp):
"""Initialize atom velocities according to Maxwell-Bolzmann,
then zero total linear and angular momenta.
Args:
atoms (ase.Atoms): initial configuration
temp (float): temperature in Kelvin
"""
from ase import units
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase.md.velocitydistribution import Stationary, ZeroRotation
MaxwellBoltzmannDistribution(atoms, temp*units.kB)
Stationary(atoms)
ZeroRotation(atoms)
def init_vel(cml):
'''
'''
arg = parse_cml_args(cml)
# read in the initial structure
init_pos = read(arg.poscar)
# set the momenta corresponding to T
MaxwellBoltzmannDistribution(init_pos,
temperature_K=arg.temperature,
force_temp=True)
# set the center-of-mass to 0
Stationary(init_pos)
# set the total angular momentum to 0
ZeroRotation(init_pos)
################################################################################
# No need to do this, use "force_temp" parameter in MaxwellBoltzmannDistribution
################################################################################
# scale the temperature to T
# vel = init_pos.get_velocities()
# Tn = init_pos.get_temperature()
# vel *= np.sqrt(arg.temperature / Tn)
# init_pos.set_velocities(vel)
# write the structure
write(arg.out, init_pos, vasp5=True, direct=True)
# units in VASP and ASE are different
vel = init_pos.get_velocities() * ase.units.fs
# np.savetxt('init_vel.dat', vel, fmt='%20.16f')
# append the velocities to the POSCAR
with open(arg.out, 'a+') as pos:
pos.write('\n')
pos.write('\n'.join(
[''.join(["%20.16f" % x for x in row]) for row in vel]))
def MD():
old_stdout = sys.stdout
sys.stdout = mystdout = StringIO()
orig_stdout = sys.stdout
f = open('out.txt', 'w')
sys.stdout = f
#f = open('out_' + str(temp) + '.txt', 'w')
#sys.stdout = f
Tmin = 1000
Tmax = 15000
a0 = 5.43
N = 1
atoms = Diamond(symbol='Si', latticeconstant=a0)
atoms *= (N, N, N)
atoms.set_calculator(model.calculator)
MaxwellBoltzmannDistribution(atoms, Tmin * units.kB) ###is this allowed?
Stationary(atoms) # zero linear momentum
ZeroRotation(atoms)
def printenergy(a=atoms):
epot = a.get_potential_energy() / len(a)
ekin = a.get_kinetic_energy() / len(a)
print(ekin)
traj = Trajectory('Si.traj', 'w', atoms)
for temp in np.linspace(Tmin, Tmax, 5):
dyn = Langevin(atoms, 5 * units.fs, units.kB * temp, 0.002)
dyn.attach(traj.write, interval=1)
dyn.attach(printenergy, interval=1)
dyn.run(100)
sys.stdout = orig_stdout
f.close()
def __init__(
self,
atomsbatch,
mdparam=DEFAULTNVEPARAMS,
):
# initialize the atoms batch system
self.atomsbatch = atomsbatch
self.mdparam = mdparam
# todo: structure optimization before starting
# intialize system momentum
MaxwellBoltzmannDistribution(self.atomsbatch,
self.mdparam['T_init'] * units.kB)
Stationary(self.atomsbatch) # zero linear momentum
ZeroRotation(self.atomsbatch)
# set thermostats
integrator = self.mdparam['thermostat']
self.integrator = integrator(self.atomsbatch,
**self.mdparam['thermostat_params'])
# attach trajectory dump
self.traj = Trajectory(self.mdparam['traj_filename'], 'w',
self.atomsbatch)
self.integrator.attach(self.traj.write,
interval=self.mdparam['save_frequency'])
# attach log file
self.integrator.attach(NeuralMDLogger(self.integrator,
self.atomsbatch,
self.mdparam['thermo_filename'],
mode='a'),
interval=self.mdparam['save_frequency'])
def __init__(self, mol, gl, path, i, Bi):
self.geom_print = gl.full_geom_print
self.printSMILES = False
self.procNum = i
self.gl = gl
self.biMolecular = Bi
self.method = gl.trajMethod
self.level = gl.trajLevel
self.mdSteps = gl.mdSteps
self.printFreq = gl.printFreq
self.mixedTimestep = gl.mixedTimestep
if self.biMolecular:
try:
self.initialT = gl.BiTemp
except:
print("No specific bimolecular temperature set, using the Langevin temperature instead")
self.initialT = gl.LTemp
else:
self.initialT = gl.LTemp
self.MDIntegrator =gl.MDIntegrator
self.timeStep = gl.timeStep * units.fs
self.eneBXD = gl.eneBXD
self.comBXD = gl.comBXD
try:
self.minCOM = gl.minCOM
except:
self.minCOM = 3.0
try:
self.fragIdx = gl.fragIndices
except:
self.fragIdx = 0
self.forces = np.zeros(mol.get_positions().shape)
self.LangFric = gl.LFric
self.LangTemp = self.initialT
self.Mol = Atoms(symbols=mol.get_chemical_symbols(), positions = mol.get_positions())
MaxwellBoltzmannDistribution(self.Mol, self.initialT * units.kB, force_temp=True)
Stationary(self.Mol)
ZeroRotation(self.Mol)
self.velocity = self.Mol.get_velocities()
print('initialising velocities for process ' + str(i) + '\n')
print(str(self.velocity) + '\n')
self.tempReactGeom = mol.copy()
self.tempProdGeom = mol.copy()
self.NEBguess = []
self.TSgeom = mol.copy()
self.productGeom = self.Mol.get_positions()
try:
self.window = gl.reactionWindow
self.endOnReac = gl.endOnReaction
self.consistantWindow = gl.reactionWindow
except:
pass
if self.biMolecular:
self.consistantWindow = int(gl.reactionWindow /4)
self.savePath = path
self.ReactionCountDown = 0
self.MolList = []
self.KeepTracking = True
self.tempList = [1000]
self.transindex = np.zeros(3)
self.numberOfSteps = 0
self.AssDisoc = False
self.TSpoint = 0
self.names=[]
self.changePoints=[]
self.adaptiveSteps = gl.maxAdapSteps
def learn_pes_by_tempering(atoms,
gp,
cutoff,
ttime,
calculator=None,
model=None,
dt=2.,
ediff=0.01,
volatile=None,
target_temperature=1000.,
stages=1,
equilibration=5,
rescale_velocities=1.05,
pressure=None,
stress_equilibration=5,
rescale_cell=1.01,
eps='random',
algorithm='fastfast',
name='model',
overwrite=True,
traj='tempering.traj',
logfile='leapfrog.log'):
"""
pressure (hydrostatic):
defined in units of Pascal and is equal to -(trace of stress tensor)/3
eps:
if 'random', strain *= a random number [0, 1)
if a positive float, strain *= 1-e^(-|dp/p|/eps) i.e. eps ~ relative p fluctuations
else, no action
"""
assert rescale_velocities > 1 and rescale_cell > 1
if pressure is not None:
warnings.warn('rescaling cell is not robust!')
if model is not None:
if type(model) == str:
model = PosteriorPotentialFromFolder(model)
if gp is None:
gp = model.gp
if atoms.get_velocities() is None:
t = target_temperature
MaxwellBoltzmannDistribution(atoms, t * units.kB)
Stationary(atoms)
ZeroRotation(atoms)
dyn = VelocityVerlet(atoms, dt * units.fs, trajectory=traj)
dyn = Leapfrog(dyn,
gp,
cutoff,
calculator=calculator,
model=model,
ediff=ediff,
volatile=volatile,
algorithm=algorithm,
logfile=logfile)
t = 0
T = '{} (instant)'.format(atoms.get_temperature())
checkpoints = np.linspace(0, ttime, stages + 1)[1:]
for k, target_t in enumerate(checkpoints):
print('stage: {}, time: {}, target time: {}, (temperature={})'.format(
k, t, target_t, T))
while t < target_t:
spu, e, T, s = dyn.run_updates(equilibration)
t += spu * equilibration * dt
dyn.rescale_velocities(
rescale_velocities if T < target_temperature else 1. /
rescale_velocities)
if pressure is not None:
spu, e, T, s = dyn.run_updates(stress_equilibration)
t += spu * stress_equilibration * dt
p = -s[:3].mean() / units.Pascal
# figure out strain
dp = p - pressure
strain = (rescale_cell if dp > 0 else 1. / rescale_cell) - 1
if eps is 'random':
strain *= np.random.uniform()
elif type(eps) == float and eps > 0 and abs(p) > 0:
strain *= 1 - np.exp(-np.abs(dp / p) / eps)
# apply strain
dyn.strain_atoms(np.eye(3) * strain)
if k == stages - 1:
dyn.model.to_folder(name,
info='temperature: {}'.format(T),
overwrite=overwrite)
else:
dyn.model.to_folder('{}_{}'.format(name, k),
info='temperature: {}'.format(T),
overwrite=overwrite)
return dyn.get_atoms(), dyn.model
def learn_pes_by_anealing(atoms,
gp,
cutoff,
calculator=None,
model=None,
dt=2.,
ediff=0.01,
volatile=None,
target_temperature=1000.,
stages=1,
equilibration=5,
rescale_velocities=1.05,
algorithm='fastfast',
name='model',
overwrite=True,
traj='anealing.traj',
logfile='leapfrog.log'):
assert rescale_velocities > 1
if model is not None:
if type(model) == str:
model = PosteriorPotentialFromFolder(model)
if gp is None:
gp = model.gp
if atoms.get_velocities() is None:
t = target_temperature / stages
MaxwellBoltzmannDistribution(atoms, t * units.kB)
Stationary(atoms)
ZeroRotation(atoms)
dyn = VelocityVerlet(atoms, dt * units.fs, trajectory=traj)
dyn = Leapfrog(dyn,
gp,
cutoff,
calculator=calculator,
model=model,
ediff=ediff,
volatile=volatile,
algorithm=algorithm,
logfile=logfile)
# initial equilibration
while dyn.volatile():
_, e, t, s = dyn.run_updates(1)
_, e, t, s = dyn.run_updates(equilibration)
temperatures = np.linspace(t, target_temperature, stages + 1)[1:]
heating = t < target_temperature
cooling = not heating
for k, target_t in enumerate(temperatures):
print(
'stage: {}, temperature: {}, target temperature: {}, ({})'.format(
k, t, target_t, 'heating' if heating else 'cooling'))
while (heating and t < target_t) or (cooling and t > target_t):
dyn.rescale_velocities(rescale_velocities if heating else 1. /
rescale_velocities)
_, e, t, s = dyn.run_updates(equilibration)
if k == stages - 1:
dyn.model.to_folder(name,
info='temperature: {}'.format(t),
overwrite=overwrite)
else:
dyn.model.to_folder('{}_{}'.format(name, k),
info='temperature: {}'.format(t),
overwrite=overwrite)
return dyn.get_atoms(), dyn.model
def train_pes_by_tempering(atoms,
gp,
cutoff,
ttime,
calculator=None,
model=None,
dt=2.,
ediff=0.01,
volatile=None,
target_temperature=1000.,
stages=1,
equilibration=5,
rescale_velocities=1.05,
pressure=None,
stress_equilibration=5,
rescale_cell=1.01,
randomize=True,
algorithm='fastfast',
name='model',
overwrite=True,
traj='tempering.traj',
logfile='leapfrog.log'):
assert rescale_velocities > 1 and rescale_cell > 1
if model is not None:
if type(model) == str:
model = PosteriorPotentialFromFolder(model)
if gp is None:
gp = model.gp
if atoms.get_velocities() is None:
t = target_temperature
MaxwellBoltzmannDistribution(atoms, t * units.kB)
Stationary(atoms)
ZeroRotation(atoms)
dyn = VelocityVerlet(atoms, dt * units.fs, trajectory=traj)
dyn = Leapfrog(dyn,
gp,
cutoff,
calculator=calculator,
model=model,
ediff=ediff,
volatile=volatile,
algorithm=algorithm,
logfile=logfile)
t = 0
T = '{} (instant)'.format(atoms.get_temperature())
checkpoints = np.linspace(0, ttime, stages + 1)[1:]
for k, target_t in enumerate(checkpoints):
print('stage: {}, time: {}, target time: {}, (temperature={})'.format(
k, t, target_t, T))
while t < target_t:
spu, e, T, s = dyn.run_updates(equilibration)
t += spu * equilibration * dt
dyn.rescale_velocities(
rescale_velocities if T < target_temperature else 1. /
rescale_velocities)
if pressure is not None:
spu, e, T, s = dyn.run_updates(stress_equilibration)
t += spu * stress_equilibration * dt
p = -s[:3].mean() / units.Pascal
# figure out factor
dp = p - pressure
factor = (rescale_cell if dp > 0 else 1. / rescale_cell)
if randomize:
factor = 1 + np.random.uniform(0, 1) * (factor - 1)
# apply rescaling
dyn.rescale_cell(factor)
if k == stages - 1:
dyn.model.to_folder(name,
info='temperature: {}'.format(T),
overwrite=overwrite)
else:
dyn.model.to_folder('{}_{}'.format(name, k),
info='temperature: {}'.format(T),
overwrite=overwrite)
return dyn.get_atoms(), dyn.model
atoms = FaceCenteredCubic('Cu',
surfaces=[[1, 0, 0], [1, 1, 0], [1, 1, 1]],
layers=(size, size, size),
vacuum=4)
# Describe the interatomic interactions with the Effective Medium Theory
atoms.set_calculator(EMT())
# Do a quick relaxation of the cluster
qn = QuasiNewton(atoms)
qn.run(0.001, 10)
# Set the momenta corresponding to T=1200K
MaxwellBoltzmannDistribution(atoms, 1200 * units.kB)
Stationary(atoms) # zero linear momentum
ZeroRotation(atoms) # zero angular momentum
# We want to run MD using the VelocityVerlet algorithm.
# Save trajectory:
dyn = VelocityVerlet(atoms, 5 * units.fs, trajectory='moldyn4.traj')
def printenergy(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)
print('Energy per atom: Epot = %.3feV Ekin = %.3feV (T=%3.0fK) '
'Etot = %.3feV' % (epot, ekin, ekin / (1.5 * units.kB), epot + ekin))
dyn.attach(printenergy, interval=10)
def test_otf_md(md_engine, super_cell, flare_calc, qe_calc):
np.random.seed(12345)
flare_calculator = flare_calc[md_engine]
# set up OTF MD engine
md_params = {
'timestep': 1 * units.fs,
'trajectory': None,
'dt': 1 * units.fs,
'externalstress': 0,
'ttime': 25,
'pfactor': 3375,
'mask': None,
'temperature': 500,
'taut': 1,
'taup': 1,
'pressure': 0,
'compressibility': 0,
'fixcm': 1,
'friction': 0.02
}
otf_params = {
'dft_calc': qe_calc,
'init_atoms': [0, 1, 2, 3],
'std_tolerance_factor': 2,
'max_atoms_added': len(super_cell.positions),
'freeze_hyps': 10,
'use_mapping': flare_calculator.use_mapping
}
# intialize velocity
temperature = md_params['temperature']
MaxwellBoltzmannDistribution(super_cell, temperature * units.kB)
Stationary(super_cell) # zero linear momentum
ZeroRotation(super_cell) # zero angular momentum
super_cell.set_calculator(flare_calculator)
test_otf = otf_md(md_engine, super_cell, md_params, otf_params)
# set up logger
otf_logger = OTFLogger(test_otf,
super_cell,
logfile=md_engine + '.log',
mode="w",
data_in_logfile=True)
test_otf.attach(otf_logger, interval=1)
# run otf
number_of_steps = 3
test_otf.otf_run(number_of_steps)
for f in glob.glob("scf.pw*"):
os.remove(f)
for f in glob.glob("*.npy"):
os.remove(f)
for f in glob.glob("kv3*"):
shutil.rmtree(f)
for f in os.listdir("./"):
if f in [f'{md_engine}.log', 'lmp.mgp']:
os.remove(f)
if f in ['out', 'otf_data']:
shutil.rmtree(f)
def test_zero_rotation(atoms):
mom1 = atoms.get_angular_momentum()
ZeroRotation(atoms)
mom2 = atoms.get_angular_momentum()
assert norm(mom1) > 0.1
assert norm(mom2) < 1e-13
#dft_calc = Espresso(pseudopotentials=dft_input['pseudopotentials'], label=dft_input['label'],
# tstress=True, tprnfor=True, nosym=True,
# input_data=input_data, kpts=dft_input['kpts'])
# -------------- set up otf npt md --------------------
timestep = 1 # fs
temperature = 100
externalstress = 0
ttime = 25
pfactor = 3375
logfile = 'al.log'
# intialize velocity
MaxwellBoltzmannDistribution(super_cell, 200 * units.kB)
Stationary(super_cell) # zero linear momentum
ZeroRotation(super_cell) # zero angular momentum
test_otf_npt = OTF_NPT(
super_cell,
timestep,
temperature,
externalstress,
ttime,
pfactor,
mask=None,
# on-the-fly parameters
dft_calc=dft_calc,
std_tolerance_factor=1,
max_atoms_added=nat,
freeze_hyps=0,
# mff parameters