"""Tools for initializing a (ML)-DFT trajectory with OF-DFT."""
from warnings import warn
from ase import units
import ase.io
from ase.md import MDLogger
from ase.md.langevin import Langevin
from ase.io.trajectory import Trajectory
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
try:
from dftpy.api.api4ase import DFTpyCalculator
from dftpy.config import DefaultOption, OptionFormat
except ModuleNotFoundError:
pass
[docs]
class OFDFTInitializer:
"""
Initializes a trajectory using OF-DFT.
Parameters
----------
parameters : mala.Parameters
MALA parameters object used to create this instance.
atoms : ase.Atoms
Initial atomic configuration for which an equilibrated configuration
is to be created.
Attributes
----------
parameters : mala.mala.common.parameters.ParametersDataGeneration
MALA data generation parameters object.
atoms : ase.Atoms
Initial atomic configuration for which an
equilibrated configuration is to be created.
dftpy_configuration : dict
Dictionary containing the DFTpy configuration. Will partially be
populated via the MALA parameters object.
"""
def __init__(self, parameters, atoms):
warn(
"The class OFDFTInitializer is experimental. The algorithms "
"within have been tested, but the API may still be subject to "
"large changes."
)
self.atoms = atoms
self.parameters = parameters.datageneration
# Check that only one element is used in the atoms.
number_of_elements = len(set([x.symbol for x in self.atoms]))
if number_of_elements > 1:
raise Exception(
"OF-DFT-MD initialization can only work with one element."
)
self.dftpy_configuration = DefaultOption()
self.dftpy_configuration["PATH"][
"pppath"
] = self.parameters.local_psp_path
self.dftpy_configuration["PP"][
self.atoms[0].symbol
] = self.parameters.local_psp_name
self.dftpy_configuration["OPT"]["method"] = self.parameters.ofdft_kedf
self.dftpy_configuration["KEDF"]["kedf"] = "WT"
self.dftpy_configuration["JOB"]["calctype"] = "Energy Force"
[docs]
def get_equilibrated_configuration(self, logging_period=None):
"""
Calculate the (OF-DFT-MD) equilibrated atomic configuration.
Parameters
----------
logging_period : int
If not None, a .log and .traj file will be filled with snapshot
information every logging_period steps.
Returns
-------
equilibrated_configuration : ase.Atoms
Equilibrated atomic configuration.
"""
# Set the DFTPy configuration.
conf = OptionFormat(self.dftpy_configuration)
# Create the DFTPy Calculator.
calc = DFTpyCalculator(config=conf)
self.atoms.set_calculator(calc)
# Create the initial velocities, and dynamics object.
MaxwellBoltzmannDistribution(
self.atoms,
temperature_K=self.parameters.ofdft_temperature,
force_temp=True,
)
dyn = Langevin(
self.atoms,
self.parameters.ofdft_timestep * units.fs,
temperature_K=self.parameters.ofdft_temperature,
friction=self.parameters.ofdft_friction,
)
# If logging is desired, do the logging.
if logging_period is not None:
dyn.attach(
MDLogger(
dyn,
self.atoms,
"mala_of_dft_md.log",
header=False,
stress=False,
peratom=True,
mode="w",
),
interval=logging_period,
)
traj = Trajectory("mala_of_dft_md.traj", "w", self.atoms)
dyn.attach(traj.write, interval=logging_period)
# Let the OF-DFT-MD run.
ase.io.write("POSCAR_initial", self.atoms, "vasp")
dyn.run(self.parameters.ofdft_number_of_timesteps)
ase.io.write("POSCAR_equilibrated", self.atoms, "vasp")