def __init__(self, atoms,
timestep, temperature, externalstress, ttime, pfactor,
mask=None, trajectory=None, logfile=None, loginterval=1):
ParallelMolDynMixin.__init__(self, 'NPT', atoms)
aseNPT.__init__(self, atoms,
timestep, temperature, externalstress, ttime, pfactor,
mask=mask, trajectory=trajectory, logfile=logfile,
loginterval=loginterval)
def __init__(self, atoms, timestep, temperature, externalstress,
ttime, pfactor, mask=None, trajectory=None, **kwargs):
NPT.__init__(self, atoms, timestep, temperature,
externalstress, ttime, pfactor, mask,
trajectory)
OTF.__init__(self, **kwargs)
self.md_engine = 'NPT'
def _molecular_dynamics(self, resume=None):
"""Performs a molecular dynamics simulation, until mdmin is
exceeded. If resuming, the file number (md%05i) is expected."""
self._log('msg', 'Molecular dynamics: md%05i' % self._counter)
mincount = 0
energies, oldpositions = [], []
thermalized = False
if resume:
self._log('msg', 'Resuming MD from md%05i.traj' % resume)
if os.path.getsize('md%05i.traj' % resume) == 0:
self._log(
'msg', 'md%05i.traj is empty. Resuming from '
'qn%05i.traj.' % (resume, resume - 1))
atoms = io.read('qn%05i.traj' % (resume - 1), index=-1)
else:
images = io.Trajectory('md%05i.traj' % resume, 'r')
for atoms in images:
energies.append(atoms.get_potential_energy())
oldpositions.append(atoms.positions.copy())
passedmin = self._passedminimum(energies)
if passedmin:
mincount += 1
self._atoms.set_momenta(atoms.get_momenta())
thermalized = True
self._atoms.positions = atoms.get_positions()
self._log('msg',
'Starting MD with %i existing energies.' % len(energies))
if not thermalized:
MaxwellBoltzmannDistribution(self._atoms,
temp=self._temperature * units.kB,
force_temp=True)
traj = io.Trajectory('md%05i.traj' % self._counter, 'a', self._atoms)
self._constrain()
dyn = NPT(self._atoms,
timestep=self._timestep * units.fs,
temperature=self._temperature * units.kB,
externalstress=self._externalstress,
ttime=self._ttime * units.fs,
pfactor=self._pfactor * units.fs**2)
# dyn = NPTber(self._atoms, timestep=self._timestep * units.fs, temperature=self._temperature, fixcm=True, pressure=self._pressure, taut=self._taut * units.fs, taup=self._taup * units.fs, compressibility=self._compressibility)
log = MDLogger(dyn,
self._atoms,
'md%05i.log' % self._counter,
header=True,
stress=False,
peratom=False)
dyn.attach(log, interval=1)
dyn.attach(traj, interval=1)
while mincount < self._mdmin:
# self._constrain()
dyn.run(1)
# del self._atoms.constraints
energies.append(self._atoms.get_potential_energy())
passedmin = self._passedminimum(energies)
if passedmin:
mincount += 1
oldpositions.append(self._atoms.positions.copy())
# Reset atoms to minimum point.
self._atoms.positions = oldpositions[passedmin[0]]
def fly(temperature, updates, atoms=None, cutoff=None, au=None, calc=None, kern=None, dt=2., tsteps=10,
ext_stress=0, pfactor=None, mask=None, ediff=0.1, fdiff=0.1, group=None, lf_kwargs={}):
atoms, model, new_model, traj, log = strategy(atoms, temperature)
if model is None:
if not kern:
kern = default_kernel(np.unique(atoms.numbers), cutoff, au=au)
else:
model = PosteriorPotentialFromFolder(model, group=group)
kern = model.gp
atoms.set_calculator(calc if calc else my_vasp())
if hasattr(pfactor, '__iter__'):
psteps, bulk_modulus = pfactor
ptime = psteps*dt
pfactor = (ptime**2)*bulk_modulus
ase_dyn = NPT(atoms, dt*units.fs, temperature*units.kB, ext_stress,
tsteps*dt*units.fs, pfactor, mask=mask, trajectory=traj)
dyn = Leapfrog(ase_dyn, kern, model=model,
algorithm='ultrafast',
ediff=ediff, fdiff=fdiff,
correct_verlet=False,
logfile=log,
group=group,
**lf_kwargs)
dyn.run_updates(updates)
dyn.model.to_folder(new_model)
if calc is None:
clean_vasp()
if group is not None:
torch.distributed.barrier()
return dyn.model
def npt_dynamics(atoms, dt, tem, bulk_modulus, stress, mask, iso, trajectory,
loginterval, append, tdamp, pdamp):
ttime = tdamp * units.fs
ptime = pdamp * units.fs
if bulk_modulus:
pfactor = (ptime**2) * bulk_modulus * units.GPa
else:
pfactor = None
configure_cell(atoms)
dyn = NPT(atoms,
dt * units.fs,
temperature_K=tem,
externalstress=stress * units.GPa,
ttime=ttime,
pfactor=pfactor,
mask=mask,
trajectory=trajectory,
append_trajectory=append,
loginterval=loginterval)
if iso:
dyn.set_fraction_traceless(0.)
return dyn
def __init__(self,
atoms,
timestep,
temperature,
externalstress,
ttime,
pfactor,
mask=None,
trajectory=None,
logfile=None,
loginterval=1):
ParallelMolDynMixin.__init__(self, 'NPT', atoms)
aseNPT.__init__(self,
atoms,
timestep,
temperature,
externalstress,
ttime,
pfactor,
mask=mask,
trajectory=trajectory,
logfile=logfile,
loginterval=loginterval)
def mlmd(atoms,
covariance=None,
calc_script=None,
dt=None,
tem=300.,
picos=100,
bulk_modulus=None,
stress=0.,
mask=None,
group=None,
tape='model.sgpr',
trajectory='md.traj',
loginterval=1,
append=False,
rattle=0.0,
ediff=0.041,
fdiff=0.082,
coveps=1e-4,
random_seed=None):
"""
atoms: ASE atoms
covariance: kernel or model
calc_script: path to a python script where the DFT calculator is defined
dt: time-step in fs
tem: temperature in Kelvin
picos: pico-seconds for md
bulk_modulus: bulk_modulus for NPT simulations. if None, NVT is performed
stress: external stress (GPa) for NPT
mask: see ase.npt.NPT
group: process-group for MPI. if None, it is initiated
tape: checkpoint for the ML potential
trajectory: traj file name
loginterval: for traj file
append: append to traj file
rattle: rattle atoms at initial step (recommended ~0.05)
ediff: energy sensitivity
fdiff: force sensitivity
coveps: skips ML update if covloss < coveps
"""
# set calculator
if calc_script is not None:
calc_script = SocketCalculator(script=calc_script)
calc = ActiveCalculator(covariance=covariance,
calculator=calc_script,
process_group=group or mpi_init(),
tape=tape,
fdiff=fdiff,
ediff=ediff,
coveps=coveps)
if random_seed:
np.random.seed(random_seed)
# process atoms
if type(atoms) == str:
if atoms.startswith('export'):
folder = atoms.split('-')[1] if '-' in atoms else 'model'
calc.model.to_folder(folder)
return
else:
atoms = read(atoms)
atoms.rattle(rattle, rng=np.random) # rattle after mpi_init
atoms.set_calculator(calc)
# define and run Nose-Hoover dynamics
if dt is None:
if (atoms.numbers == 1).any():
dt = 0.25
else:
dt = 1.
ttime = 25 * units.fs
ptime = 100 * units.fs
if bulk_modulus:
pfactor = (ptime**2) * bulk_modulus * units.GPa
else:
pfactor = None
init_velocities(atoms, tem)
make_cell_upper_triangular(atoms)
filtered = FilterDeltas(atoms)
steps_for_1ps = int(1000 / dt)
dyn = NPT(filtered,
dt * units.fs,
tem * units.kB,
stress * units.GPa,
ttime,
pfactor,
mask=mask,
trajectory=trajectory,
append_trajectory=append,
loginterval=loginterval)
dyn.run(picos * steps_for_1ps)
from ase.io.trajectory import Trajectory
from ase.io import read
from ase.md.npt import NPT
from ase.units import fs, kB
atoms = read('Na_in_water.xyz')
calc = GPAW(mode='lcao',
xc='PBE',
basis='dzp',
symmetry={'point_group': False},
charge=1,
txt='gpawOutput.gpaw-out')
atoms.set_calculator(calc)
dyn = NPT(atoms,
pfactor=None,
externalstress=0,
temperature=350*kB,
timestep=0.5*fs,
ttime=20*fs,
logfile='mdOutput.log')
trajectory = Trajectory('nptDyn.traj', 'w', atoms)
dyn.attach(trajectory.write, interval=1)
dyn.run(4000)
def __init__(self, atoms, *args, **kwargs):
aseNPT.__init__(self, atoms, *args, **kwargs)
if getattr(atoms, "parallel", False):
raise NotImplementedError("NPT dynamics does not work in parallel with this version of ASE. Please use the developer version.")
)
start = time.time()
calc = GPAW(mode='lcao',
xc='PBE',
basis='dzp',
symmetry={'point_group': False},
charge=1,
h=0.2,
txt='out_mdTask1.txt')
a.set_calculator(calc)
dyn = NPT(atoms=a,
timestep=dt,
temperature=350 * kB,
externalstress=0,
ttime=20 * fs,
pfactor=None,
logfile='log_mdTask1.txt') # Using the Nosé-Hoover thermostat
trajectory = Trajectory('mdTask1.traj', 'w', a)
dyn.attach(
trajectory.write,
interval=1) # Write state of the system to trajectory at every timestep
dyn.run(N_steps)
end = time.time()
if world.rank == 0:
print('-------- MD simulation finished in: ' +
f'{(end-start):.2f} s --------'.rjust(34))
print(
'----------------------------------------------------------------------'
# atoms,
# d_t,
# trajectory = label+'.traj',
# logfile = 'log_'+label+'.txt',
# )
# from ase.md import Langevin
# dyn = Langevin(
# atoms = atoms,
# timestep = 10 *units.fs,
# temperature_K = temp,
# friction = 1e-02,
# trajectory = label+'.traj',
# logfile = 'log_'+label+'.txt',
# )
from ase.md.npt import NPT
dyn = NPT(
atoms=atoms,
timestep=d_t,
temperature_K=temp,
externalstress=0.,
ttime=75 * units.fs,
pfactor=(75. * units.fs)**2 * 100. * units.GPa,
trajectory=label + '.traj',
logfile='log_' + label + '.txt',
)
### relax option
dyn.set_fraction_traceless(0) # 0 --> no shape change but yes volume change
dyn.run(
steps=t_step
) #MD simulation of object 'dyn' is performed by 'run' method of VV class
def __init__(self, atoms, *args, **kwargs):
aseNPT.__init__(self, atoms, *args, **kwargs)
if getattr(atoms, "parallel", False):
raise NotImplementedError(
"NPT dynamics does not work in parallel with this version of ASE. Please use the developer version."
)
os.remove(ase_db)
kspacing = 0.2
steps = 10
struc = bulk("Si", 'diamond', a=5.468728, cubic=True)
mliap = "potentials/Si-20-20.pth"
ff = PyXtal_FF(model={'system': ["Si"]}, logo=False)
ff.run(mode='predict', mliap=mliap)
calc = PyXtalFFCalculator(ff=ff)
struc.set_calculator(calc)
#struc.set_calculator(set_vasp('single', kspacing))
#energy, forces = dft_run(struc, path=dumpfolder, ncpu=16, max_time=10) # total energy
# MD
traj = Trajectory("Si.traj", 'w', struc)
MaxwellBoltzmannDistribution(struc, temperature_K=300)
dyn = NPT(struc,
0.1 * units.fs,
externalstress=0.,
ttime=1000 * units.fs,
pfactor=1000 * units.fs,
temperature_K=300)
dyn.attach(traj.write, interval=steps)
for i in range(1000):
dyn.run(steps=steps)
PrintEnergy(struc)
save_to_ASE_db(struc, path=ase_db)
traj.close()
def init_md(
self,
name,
time_step=0.5,
temp_init=300,
temp_bath=None,
external_stress=None,
ttime=None,
pfactor=None,
reset=False,
interval=1,
):
"""
Initialize an ase molecular dynamics trajectory. The logfile needs to
be specifies, so that old trajectories are not overwritten. This
functionality can be used to subsequently carry out equilibration and
production.
Args:
name (str): Basic name of logfile and trajectory
time_step (float): Time step in fs (default=0.5)
temp_init (float): Initial temperature of the system in K
(default is 300)
temp_bath (float): Carry out Langevin NVT dynamics at the specified
temperature. If set to None, NVE dynamics are performed
instead (default=None)
reset (bool): Whether dynamics should be restarted with new initial
conditions (default=False)
interval (int): Data is stored every interval steps (default=1)
"""
# If a previous dynamics run has been performed, don't reinitialize
# velocities unless explicitly requested via restart=True
if not self.dynamics or reset:
self._init_velocities(temp_init=temp_init)
# Set up dynamics
if temp_bath is None and external_stress is None:
self.dynamics = VelocityVerlet(self.molecule, time_step * units.fs)
elif external_stress is None:
self.dynamics = Langevin(
self.molecule,
time_step * units.fs,
temp_bath * units.kB,
1.0 / (100.0 * units.fs),
)
else:
self.dynamics = NPT(
self.molecule,
time_step * units.fs,
temp_bath * units.kB,
external_stress * units.GPa,
ttime * units.fs,
pfactor * units.fs**2 * units.GPa,
)
# Create monitors for logfile and a trajectory file
logfile = os.path.join(self.working_dir, "%s.log" % name)
trajfile = os.path.join(self.working_dir, "%s.traj" % name)
logger = MDLogger(
self.dynamics,
self.molecule,
logfile,
stress=False,
peratom=False,
header=True,
mode="a",
)
trajectory = Trajectory(trajfile, "w", self.molecule)
# Attach monitors to trajectory
self.dynamics.attach(logger, interval=interval)
self.dynamics.attach(trajectory.write, interval=interval)
class AseInterface:
"""
Interface for ASE calculations (optimization and molecular dynamics)
Args:
molecule_path (str): Path to initial geometry
ml_model (object): Trained model
working_dir (str): Path to directory where files should be stored
device (str): cpu or cuda
"""
def __init__(
self,
molecule_path,
ml_model,
working_dir,
device="cpu",
energy="energy",
forces="forces",
energy_units="eV",
forces_units="eV/Angstrom",
environment_provider=SimpleEnvironmentProvider(),
):
# Setup directory
self.working_dir = working_dir
if not os.path.exists(self.working_dir):
os.makedirs(self.working_dir)
# Load the molecule
self.molecule = None
self._load_molecule(molecule_path)
# Set up calculator
calculator = SpkCalculator(
ml_model,
device=device,
energy=energy,
forces=forces,
energy_units=energy_units,
forces_units=forces_units,
environment_provider=environment_provider,
)
self.molecule.set_calculator(calculator)
# Unless initialized, set dynamics to False
self.dynamics = False
def _load_molecule(self, molecule_path):
"""
Load molecule from file (can handle all ase formats).
Args:
molecule_path (str): Path to molecular geometry
"""
file_format = os.path.splitext(molecule_path)[-1]
if file_format == "xyz":
self.molecule = read_xyz(molecule_path)
else:
self.molecule = read(molecule_path)
def save_molecule(self, name, file_format="xyz", append=False):
"""
Save the current molecular geometry.
Args:
name (str): Name of save-file.
file_format (str): Format to store geometry (default xyz).
append (bool): If set to true, geometry is added to end of file
(default False).
"""
molecule_path = os.path.join(self.working_dir,
"%s.%s" % (name, file_format))
if file_format == "xyz":
# For extended xyz format, plain is needed since ase can not parse
# the extxyz it writes
write_xyz(molecule_path, self.molecule, plain=True)
else:
write(molecule_path,
self.molecule,
format=file_format,
append=append)
def calculate_single_point(self):
"""
Perform a single point computation of the energies and forces and
store them to the working directory. The format used is the extended
xyz format. This functionality is mainly intended to be used for
interfaces.
"""
energy = self.molecule.get_potential_energy()
forces = self.molecule.get_forces()
self.molecule.energy = energy
self.molecule.forces = forces
self.save_molecule("single_point", file_format="extxyz")
def init_md(
self,
name,
time_step=0.5,
temp_init=300,
temp_bath=None,
external_stress=None,
ttime=None,
pfactor=None,
reset=False,
interval=1,
):
"""
Initialize an ase molecular dynamics trajectory. The logfile needs to
be specifies, so that old trajectories are not overwritten. This
functionality can be used to subsequently carry out equilibration and
production.
Args:
name (str): Basic name of logfile and trajectory
time_step (float): Time step in fs (default=0.5)
temp_init (float): Initial temperature of the system in K
(default is 300)
temp_bath (float): Carry out Langevin NVT dynamics at the specified
temperature. If set to None, NVE dynamics are performed
instead (default=None)
reset (bool): Whether dynamics should be restarted with new initial
conditions (default=False)
interval (int): Data is stored every interval steps (default=1)
"""
# If a previous dynamics run has been performed, don't reinitialize
# velocities unless explicitly requested via restart=True
if not self.dynamics or reset:
self._init_velocities(temp_init=temp_init)
# Set up dynamics
if temp_bath is None and external_stress is None:
self.dynamics = VelocityVerlet(self.molecule, time_step * units.fs)
elif external_stress is None:
self.dynamics = Langevin(
self.molecule,
time_step * units.fs,
temp_bath * units.kB,
1.0 / (100.0 * units.fs),
)
else:
self.dynamics = NPT(
self.molecule,
time_step * units.fs,
temp_bath * units.kB,
external_stress * units.GPa,
ttime * units.fs,
pfactor * units.fs**2 * units.GPa,
)
# Create monitors for logfile and a trajectory file
logfile = os.path.join(self.working_dir, "%s.log" % name)
trajfile = os.path.join(self.working_dir, "%s.traj" % name)
logger = MDLogger(
self.dynamics,
self.molecule,
logfile,
stress=False,
peratom=False,
header=True,
mode="a",
)
trajectory = Trajectory(trajfile, "w", self.molecule)
# Attach monitors to trajectory
self.dynamics.attach(logger, interval=interval)
self.dynamics.attach(trajectory.write, interval=interval)
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 run_md(self, steps):
"""
Perform a molecular dynamics simulation using the settings specified
upon initializing the class.
Args:
steps (int): Number of simulation steps performed
"""
if not self.dynamics:
raise AttributeError("Dynamics need to be initialized using the"
" 'setup_md' function")
self.dynamics.run(steps)
def optimize(self, fmax=1.0e-2, steps=1000):
"""
Optimize a molecular geometry using the Quasi Newton optimizer in ase
(BFGS + line search)
Args:
fmax (float): Maximum residual force change (default 1.e-2)
steps (int): Maximum number of steps (default 1000)
"""
name = "optimization"
optimize_file = os.path.join(self.working_dir, name)
optimizer = QuasiNewton(
self.molecule,
trajectory="%s.traj" % optimize_file,
restart="%s.pkl" % optimize_file,
)
optimizer.run(fmax, steps)
# Save final geometry in xyz format
self.save_molecule(name)
def compute_normal_modes(self, write_jmol=True):
"""
Use ase calculator to compute numerical frequencies for the molecule
Args:
write_jmol (bool): Write frequencies to input file for
visualization in jmol (default=True)
"""
freq_file = os.path.join(self.working_dir, "normal_modes")
# Compute frequencies
frequencies = Vibrations(self.molecule, name=freq_file)
frequencies.run()
# Print a summary
frequencies.summary()
# Write jmol file if requested
if write_jmol:
frequencies.write_jmol()
npt = False
tem = 1000.
stress = 0.
dt = 1.
ttime = 25 * units.fs
ptime = 100 * units.fs
bulk_modulus = 137.
pfactor = (ptime**2) * bulk_modulus * units.GPa
init_velocities(atoms, tem)
# make_cell_upper_triangular(atoms)
filtered = FilterDeltas(atoms)
dyn = NPT(filtered,
dt * units.fs,
temperature_K=tem,
externalstress=stress * units.GPa,
ttime=ttime,
pfactor=pfactor if npt else None,
mask=None,
trajectory='md.traj',
append_trajectory=False,
loginterval=1)
# F. run md
dyn.run(1000)
# G. save model manually (necessary only if pckl=None above)
# calc.model.to_folder('model.pckl')
# H. restart
# calc = ActiveCalculator(covariance='model.pckl/', ...)
# I. using the saved tapes (.sgpr files)