def __init__(self,
structure,
workdir='.',
target_forces=1E-3,
waiting=False,
executable='vasp',
encut=1.3,
kp_grid=None,
kp_density=1E4,
relax_cell=True):
Relaxator.__init__(self, target_forces)
self.target_forces = target_forces
self.waiting = waiting
self.vaspjob = VaspJob(workdir=workdir, executable=executable)
self.relaxed = False
if kp_grid is not None:
self.kpoints = KPoints(kmode='gamma', grid=kp_grid)
else:
self.kpoints = KPoints.optimized_grid(structure.lattice,
kp_density=kp_density)
self.vaspjob.initialize(structure=structure, kpoints=self.kpoints)
self.encut = encut
self.relax_cell = relax_cell
task_params = {
'target_forces': self.target_forces,
'encut': self.encut,
'relax_cell': self.relax_cell
}
Task.__init__(self,
structure=structure,
task_params=task_params,
workdir=workdir,
executable=executable)
self.stage = 1
def __init__(self, structure, workdir='.', target_forces=1E-3, executable='vasp',
encut=1.3, kp_grid=None, kp_density=1E4, relax_cell=True,
max_calls=10,pspdir='potpaw_PBE', psp_options=None, extra_vars=None, heterostructure=False):
Relaxator.__init__(self, target_forces)
self.target_forces = target_forces
# If heterostructure is true it will keep the repeating order found
# in the POSCAR.
# Added by Uthpala on Apr 20th, 2020.
self.heterostructure = heterostructure
self.vaspjob = VaspJob(executable=executable, workdir=workdir)
self.relaxed = False
if kp_grid is not None:
self.kpoints = KPoints(kmode='gamma', grid=kp_grid)
else:
self.kpoints = KPoints.optimized_grid(structure.lattice, kp_density=kp_density)
self.vaspjob.initialize(structure=structure, kpoints=self.kpoints, pspdir=pspdir, heterostructure=self.heterostructure)
self.vaspjob.potcar_setup = psp_options
self.encut = encut
self.relax_cell = relax_cell
self.max_calls = max_calls
self.pspdir = pspdir
if extra_vars is not None:
self.extra_vars = extra_vars
else:
self.extra_vars = {}
task_params = {'target_forces': self.target_forces, 'encut': self.encut, 'relax_cell': self.relax_cell,
'max_calls': self.max_calls}
Task.__init__(self, structure=structure, task_params=task_params, workdir=workdir, executable=executable)
def run(self):
n = 10
dftb = DFTBplus()
kpoints = KPoints.optimized_grid(self.structure.lattice, kp_density=10000, force_odd=True)
dftb.initialize(workdir=self.workdir, structure=self.structure, kpoints=kpoints)
ans = dftb.set_slater_koster(search_paths=self.slater_path)
if not ans:
print('Slater-Koster files not complete')
return
grid = None
energies = []
while True:
density = n ** 3
kpoints = KPoints.optimized_grid(self.structure.lattice, kp_density=density, force_odd=True)
if np.sum(grid) != np.sum(kpoints.grid):
pcm_log.debug('Trial density: %d Grid: %s' % (density, kpoints.grid))
grid = list(kpoints.grid)
dftb.kpoints = kpoints
dftb.basic_input()
dftb.hamiltonian['MaxSCCIterations'] = 50
if os.path.isfile('charges.bin'):
dftb.hamiltonian['ReadInitialCharges'] = True
dftb.hamiltonian['Mixer'] = {'name': 'DIIS'}
dftb.set_static()
dftb.set_inputs()
dftb.run()
if self.waiting:
dftb.runner.wait()
while True:
if dftb.runner is not None and dftb.runner.poll() is not None:
pcm_log.info('Execution completed. Return code %d' % dftb.runner.returncode)
filename = dftb.workdir + os.sep + 'detailed.out'
if os.path.exists(filename):
ret = read_detailed_out(filename)
line = 'KPoint_grid= %15s iSCC= %4d Total_energy= %10.4f SCC_error= %9.3E'
print(line % (grid, ret['SCC']['iSCC'], ret['total_energy'], ret['SCC']['SCC_error']))
else:
print('detailed.out could not be found, exiting...')
return
n += 2
energies.append(ret['total_energy'])
break
time.sleep(10)
self.results.append({'kp_grid': grid,
'iSCC': ret['SCC']['iSCC'],
'Total_energy': ret['total_energy'],
'SCC_error': ret['SCC']['SCC_error']})
else:
n += 2
if len(energies) > 2 and abs(max(energies[-3:]) - min(energies[-3:])) < self.energy_tolerance:
break
def __init__(self, structure, workdir='.', kpoints=None, executable='vasp', energy_tolerance=1E-3,
increment_factor=0.2, initial_encut=1.3, pspdir='potpaw_PBE', psp_options=None, extra_vars=None, heterostructure=False):
self.structure = structure
self.workdir = workdir
self.executable = executable
self.increment_factor = increment_factor
self.initial_encut = initial_encut
self.pspdir = pspdir
if psp_options is not None:
self.psp_options = psp_options
else:
self.psp_options = {}
if extra_vars is not None:
self.extra_vars = extra_vars
else:
self.extra_vars = {}
if kpoints is None:
kp = KPoints.optimized_grid(self.structure.lattice, kp_density=1E4, force_odd=True)
self.kpoints = kp
else:
self.kpoints = kpoints
# If heterostructure is true it will keep the repeating order found
# in the POSCAR.
# Added by Uthpala on Apr 20th, 2020.
self.heterostructure = heterostructure
Convergence.__init__(self, energy_tolerance)
self.task_params = {'energy_tolerance': self.energy_tolerance, 'increment_factor': self.increment_factor,
'initial_encut': self.initial_encut}
Task.__init__(self, structure=structure, task_params=self.task_params, workdir=workdir, executable=executable)
def __init__(self,
structure,
workdir='.',
binary='vasp',
encut=1.3,
kpoints=None,
kp_density=1E4):
self.encut = encut
if kpoints is None:
kp = KPoints.optimized_grid(structure.lattice,
kp_density=kp_density,
force_odd=True)
self.kpoints = kp
else:
self.kpoints = kpoints
self.task_params = {
'encut': self.encut,
'kpoints': self.kpoints.to_dict
}
Task.__init__(self,
structure=structure,
task_params=self.task_params,
workdir=workdir,
binary=binary)
def __init__(self,
structure,
relaxator_params=None,
workdir='.',
kpoints=None,
target_forces=1E-3,
waiting=False,
kp_density=10000,
forced=True):
self.workdir = workdir
self.initial_structure = structure
self.slater_path = None
self.symmetrize = False
self.forced = forced
if relaxator_params is None:
relaxator_params = {'slater_path': '.'}
self.set_params(relaxator_params)
if self.symmetrize:
self.initial_structure = symmetrize(structure)
self.structure = self.initial_structure.copy()
self.waiting = waiting
if kpoints is None:
self.kpoints = KPoints.optimized_grid(self.structure.lattice,
kp_density=kp_density)
else:
self.kpoints = kpoints
Relaxator.__init__(self, target_forces)
def __init__(self,
structure,
workdir='.',
slater_path='.',
waiting=False,
energy_tolerance=1E-3,
output_file='results.json'):
self.structure = structure
self.workdir = workdir
self.slater_path = slater_path
self.waiting = waiting
self.energy_tolerance = energy_tolerance
if isinstance(slater_path, str):
self.slater_path = [slater_path]
self.results = []
self.output_file = output_file
dftb = DFTBplus(workdir=self.workdir)
kpoints = KPoints.optimized_grid(self.structure.lattice,
kp_density=10000,
force_odd=True)
dftb.initialize(structure=self.structure, kpoints=kpoints)
ans = dftb.set_slater_koster(search_paths=self.slater_path)
if not ans:
print('Slater-Koster files not complete')
def __init__(self,
structure,
workdir='.',
binary='abinit',
ecut=50,
kpoints=None,
kp_density=1E4):
self.ecut = ecut
if kpoints is None:
kp = KPoints.optimized_grid(structure.lattice,
kp_density=kp_density,
force_odd=True)
self.kpoints = kp
else:
self.kpoints = kpoints
self.task_params = {'ecut': self.ecut, 'kpoints': self.kpoints.to_dict}
Task.__init__(self,
structure=structure,
task_params=self.task_params,
workdir=workdir,
binary=binary)
self.abinitjob = AbinitJob()
self.abinitjob.initialize(workdir=workdir,
structure=structure,
binary=binary)
def __init__(
self,
structure,
workdir=".",
kpoints=None,
executable="vasp",
energy_tolerance=1e-3,
increment_factor=0.2,
initial_encut=1.3,
pspdir="potpaw_PBE",
psp_options=None,
extra_vars=None,
heterostructure=False,
make_potcar=True,
):
self.structure = structure
self.workdir = workdir
self.executable = executable
self.increment_factor = increment_factor
self.initial_encut = initial_encut
self.pspdir = pspdir
if psp_options is not None:
self.psp_options = psp_options
else:
self.psp_options = {}
if extra_vars is not None:
self.extra_vars = extra_vars
else:
self.extra_vars = {}
if kpoints is None:
kp = KPoints.optimized_grid(self.structure.lattice,
kp_density=1e4,
force_odd=True)
self.kpoints = kp
else:
self.kpoints = kpoints
# If heterostructure is true it will keep the repeating order found
# in the POSCAR.
# Added by Uthpala on Apr 20th, 2020.
self.heterostructure = heterostructure
# setting make_potcar=False will not generate a new POTCAR.
# Added by Uthpala on May 12th, 2021.
self.make_potcar = make_potcar
Convergence.__init__(self, energy_tolerance)
self.task_params = {
"energy_tolerance": self.energy_tolerance,
"increment_factor": self.increment_factor,
"initial_encut": self.initial_encut,
}
Task.__init__(
self,
structure=structure,
task_params=self.task_params,
workdir=workdir,
executable=executable,
)
def __init__(self,
structure,
workdir='.',
kpoints=None,
binary='vasp',
energy_tolerance=1E-3,
increment_factor=0.2,
initial_encut=1.3):
self.structure = structure
self.workdir = workdir
self.binary = binary
self.increment_factor = increment_factor
self.initial_encut = initial_encut
if kpoints is None:
kp = KPoints.optimized_grid(self.structure.lattice,
kp_density=1E4,
force_odd=True)
self.kpoints = kp
else:
self.kpoints = kpoints
Convergence.__init__(self, energy_tolerance)
self.task_params = {
'energy_tolerance': self.energy_tolerance,
'increment_factor': self.increment_factor,
'initial_encut': self.initial_encut
}
Task.__init__(self,
structure=structure,
task_params=self.task_params,
workdir=workdir,
binary=binary)
def initialize(self, structure, kpoints=None, kp_density=10000):
assert structure.is_crystal
assert structure.is_perfect
self.structure = structure
self.get_geometry()
self.kp_density = kp_density
if kpoints is None:
kpoints = KPoints.optimized_grid(self.structure.lattice, kp_density=self.kp_density, force_odd=True)
self.kpoints = kpoints
def best_kpoints(self):
if not self.is_converge:
print('Convergence not completed')
return None
else:
kp = KPoints.optimized_grid(self.structure.lattice, kp_density=self.convergence_info[-3]['kp_density'],
force_odd=True)
return kp
def best_kpoints(self):
if not self.is_converge:
print('Convergence not completed')
return None
else:
kp = KPoints.optimized_grid(self.structure.lattice, kp_density=self.convergence_info[-3]['kp_density'],
force_odd=True)
return kp
def load(self, filename=None):
if filename is None:
filename = self.workdir + os.sep + 'task.json'
rf = open(filename)
data = json.load(rf)
rf.close()
self.task_params = data['task_params']
self.output = data['output']
self.encut = self.task_params['encut']
self.kpoints = KPoints.from_dict(self.task_params['kpoints'])
def load(self, filename=None):
if filename is None:
filename = self.workdir + os.sep + 'task.json'
rf = open(filename)
data = json.load(rf)
rf.close()
self.task_params = data['task_params']
self.output = data['output']
self.ecut = self.task_params['ecut']
self.kpoints = KPoints.from_dict(self.task_params['kpoints'])
def initialize(self, structure, kpoints=None, kp_density=10000):
assert structure.is_crystal
assert structure.is_perfect
self.structure = structure
self.get_geometry()
self.kp_density = kp_density
if kpoints is None:
kpoints = KPoints.optimized_grid(self.structure.lattice,
kp_density=self.kp_density,
force_odd=True)
self.kpoints = kpoints
def __init__(self, structure, workdir='.', executable='vasp', encut=1.3, kpoints=None, kp_density=1E4,
extra_incar=None):
self.encut = encut
if kpoints is None:
kp = KPoints.optimized_grid(structure.lattice, kp_density=kp_density, force_odd=True)
self.kpoints = kp
else:
self.kpoints = kpoints
self.task_params = {'encut': self.encut, 'kpoints': self.kpoints.to_dict, 'extra_incar': extra_incar}
Task.__init__(self, structure=structure, task_params=self.task_params, workdir=workdir, executable=executable)
def __init__(self, structure, workdir='.', tolmxf=1E-4, tolrff=1E-2, waiting=False, executable='abinit',
ecut=50, kp_grid=None, kp_density=1E4, relax_cell=True, max_calls=10):
Relaxator.__init__(self, tolmxf)
self.tolmxf = tolmxf
self.ecut = ecut
self.waiting = waiting
self.abinitjob = AbinitJob(workdir=workdir, executable=executable)
self.relaxed = False
self.tolmxf = tolmxf
self.tolrff = tolrff
if kp_grid is not None:
self.kpoints = KPoints(kmode='gamma', grid=kp_grid)
else:
self.kpoints = KPoints.optimized_grid(structure.lattice, kp_density=kp_density)
self.abinitjob.initialize(structure=structure)
self.relax_cell = relax_cell
self.max_calls = max_calls
task_params = {'tolmxf': self.target_forces, 'ecut': self.ecut, 'relax_cell': self.relax_cell,
'max_calls': self.max_calls}
Task.__init__(self, structure=structure, task_params=task_params, workdir=workdir, executable=executable)
def __init__(self, structure, workdir='.', binary='abinit', ecut=50, kpoints=None, kp_density=1E4):
self.ecut = ecut
if kpoints is None:
kp = KPoints.optimized_grid(structure.lattice, kp_density=kp_density, force_odd=True)
self.kpoints = kp
else:
self.kpoints = kpoints
self.task_params = {'ecut': self.ecut, 'kpoints': self.kpoints.to_dict}
Task.__init__(self, structure=structure, task_params=self.task_params, workdir=workdir, binary=binary)
self.abinitjob = AbinitJob()
self.abinitjob.initialize(workdir=workdir, structure=structure, binary=binary)
def initialize(self, structure, workdir='.', kpoints=None, binary='dftb+', kp_density=10000):
assert structure.is_crystal
assert structure.is_perfect
self.structure = structure
self.get_geometry()
self.workdir = workdir
if not os.path.lexists(workdir):
os.mkdir(workdir)
self.binary = binary
self.kp_density = kp_density
if kpoints is None:
kpoints = KPoints.optimized_grid(self.structure.lattice, kp_density=self.kp_density, force_odd=True)
self.kpoints = kpoints
def __init__(self, structure, workdir, slater_path, waiting=False, kpoints=None, output_file='results.json',
max_scc_iterations=50):
self.structure = structure
self.workdir = workdir
self.slater_path = slater_path
self.waiting = waiting
self.MaxSCCIterations = max_scc_iterations
if isinstance(slater_path, str):
self.slater_path = [slater_path]
self.results = []
self.output_file = output_file
if kpoints is None:
self.kpoints = KPoints.optimized_grid(self.structure.lattice, kp_density=10000, force_odd=True)
else:
self.kpoints = kpoints
def __init__(self, structure, workdir, slater_path, waiting=False, kpoints=None, output_file='results.json',
max_scc_iterations=50):
self.structure = structure
self.workdir = workdir
self.slater_path = slater_path
self.waiting = waiting
self.MaxSCCIterations = max_scc_iterations
if isinstance(slater_path, str):
self.slater_path = [slater_path]
self.results = []
self.output_file = output_file
if kpoints is None:
self.kpoints = KPoints.optimized_grid(self.structure.lattice, kp_density=10000, force_odd=True)
else:
self.kpoints = kpoints
def __init__(self, structure, workdir='.', kpoints=None, binary='vasp', energy_tolerance=1E-3,
increment_factor=0.2, initial_encut=1.3):
self.structure = structure
self.workdir = workdir
self.binary = binary
self.increment_factor = increment_factor
self.initial_encut = initial_encut
if kpoints is None:
kp = KPoints.optimized_grid(self.structure.lattice, kp_density=1E4, force_odd=True)
self.kpoints = kp
else:
self.kpoints = kpoints
Convergence.__init__(self, energy_tolerance)
self.task_params = {'energy_tolerance': self.energy_tolerance, 'increment_factor': self.increment_factor,
'initial_encut': self.initial_encut}
Task.__init__(self, structure=structure, task_params=self.task_params, workdir=workdir, binary=binary)
def __init__(self, structure, workdir='.', target_forces=1E-3, waiting=False, binary='vasp',
encut=1.3, kp_grid=None, kp_density=1E4, relax_cell=True):
Relaxator.__init__(self, target_forces)
self.target_forces = target_forces
self.waiting = waiting
self.vaspjob = VaspJob()
self.relaxed = False
if kp_grid is not None:
self.kpoints = KPoints(kmode='gamma', grid=kp_grid)
else:
self.kpoints = KPoints.optimized_grid(structure.lattice, kp_density=kp_density)
self.vaspjob.initialize(workdir=workdir, structure=structure, kpoints=self.kpoints, binary=binary)
self.encut = encut
self.relax_cell = relax_cell
task_params = {'target_forces': self.target_forces, 'encut': self.encut, 'relax_cell': self.relax_cell}
Task.__init__(self, structure=structure, task_params=task_params, workdir=workdir, binary=binary)
self.stage = 1
def prepare_folder(self, entry_id, workdir, binary='vasp', source_dir='.'):
vj = VaspJob()
structure = self.get_structure(entry_id)
kp = KPoints.optimized_grid(structure.lattice, kp_density=2E4)
vj.initialize(structure, workdir=workdir, kpoints=kp, binary=binary)
vj.clean()
vj.input_variables = read_incar(source_dir + '/INCAR')
magmom_sph = self.get_entry(entry_id, {'properties.magmom': 1})['properties']['magmom']
magmom_car = spherical_to_cartesian(magmom_sph)
vj.input_variables.variables['MAGMOM'] = [float(x) for x in magmom_car.flatten()]
vj.input_variables.variables['M_CONSTR'] = [float(x) for x in magmom_car.flatten()]
vj.input_variables.variables['IBRION'] = -1
vj.input_variables.variables['LWAVE'] = True
vj.input_variables.variables['EDIFF'] = 1E-5
vj.input_variables.variables['LAMBDA'] = 10
vj.input_variables.variables['NSW'] = 0
vj.input_variables.variables['I_CONSTRAINED_M'] = 1
vj.set_inputs()
def __init__(self, structure, workdir='.', slater_path='.', waiting=False, energy_tolerance=1E-3,
output_file='results.json'):
self.structure = structure
self.workdir = workdir
self.slater_path = slater_path
self.waiting = waiting
self.energy_tolerance = energy_tolerance
if isinstance(slater_path, str):
self.slater_path = [slater_path]
self.results = []
self.output_file = output_file
dftb = DFTBplus()
kpoints = KPoints.optimized_grid(self.structure.lattice, kp_density=10000, force_odd=True)
dftb.initialize(workdir=self.workdir, structure=self.structure, kpoints=kpoints)
ans = dftb.set_slater_koster(search_paths=self.slater_path)
if not ans:
print('Slater-Koster files not complete')
def create_inputs(self, kp_density=10000, encut=1.0):
# kpoints = KPoints(kmode='gamma', grid=[4, 4, 4])
for entry in self.population.pcdb.entries.find():
name = str(entry['_id'])
workdir = self.basedir + os.sep + name
structure = self.population.db.get_structure(entry['_id'])
kpoints = KPoints.optimized_grid(structure.lattice, kp_density=kp_density)
print(kpoints)
vj = VaspJob()
vj.initialize(workdir=workdir, structure=structure, kpoints=kpoints)
inp = InputVariables()
inp.set_rough_relaxation()
vj.set_input_variables(inp)
vj.write_potcar()
vj.input_variables.set_encut(ENCUT=encut, POTCAR=workdir + os.sep + 'POTCAR')
vj.set_inputs()
self.vasp_jobs[name] = vj
self.runs[name] = 0
self.status[name] = ['ACTIVE']
def __init__(self, structure, workdir='.', tolmxf=1E-4, tolrff=1E-2, waiting=False, binary='abinit',
ecut=50, kp_grid=None, kp_density=1E4, relax_cell=True, max_calls=10):
Relaxator.__init__(self, tolmxf)
self.tolmxf = tolmxf
self.ecut = ecut
self.waiting = waiting
self.abinitjob = AbinitJob()
self.relaxed = False
if kp_grid is not None:
self.kpoints = KPoints(kmode='gamma', grid=kp_grid)
else:
self.kpoints = KPoints.optimized_grid(structure.lattice, kp_density=kp_density)
self.abinitjob.initialize(workdir=workdir, structure=structure, binary=binary)
self.relax_cell = relax_cell
self.max_calls = max_calls
task_params = {'tolmxf': self.target_forces, 'ecut': self.ecut, 'relax_cell': self.relax_cell,
'max_calls': self.max_calls}
Task.__init__(self, structure=structure, task_params=task_params, workdir=workdir, binary=binary)
def create_inputs(self, kp_density=10000, encut=1.0):
# kpoints = KPoints(kmode='gamma', grid=[4, 4, 4])
for entry in self.population.pcdb.entries.find():
name = str(entry['_id'])
workdir = self.basedir + os.sep + name
structure = self.population.db.get_structure(entry['_id'])
kpoints = KPoints.optimized_grid(structure.lattice,
kp_density=kp_density)
print(kpoints)
vj = VaspJob(workdir=workdir)
vj.initialize(structure=structure, kpoints=kpoints)
inp = VaspInput()
inp.set_rough_relaxation()
vj.set_input_variables(inp)
vj.write_potcar()
vj.input_variables.set_encut(ENCUT=encut,
POTCAR=workdir + os.sep + 'POTCAR')
vj.set_inputs()
self.vasp_jobs[name] = vj
self.runs[name] = 0
self.status[name] = ['ACTIVE']
def __init__(self, structure, relaxator_params=None, workdir='.', kpoints=None, target_forces=1E-3, waiting=False,
kp_density=10000, forced=True):
self.workdir = workdir
self.initial_structure = structure
self.slater_path = None
self.symmetrize = False
self.forced = forced
if relaxator_params is None:
relaxator_params = {'slater_path': '.'}
self.set_params(relaxator_params)
if self.symmetrize:
self.initial_structure = symmetrize(structure)
self.structure = self.initial_structure.copy()
self.waiting = waiting
if kpoints is None:
self.kpoints = KPoints.optimized_grid(self.structure.lattice, kp_density=kp_density)
else:
self.kpoints = kpoints
Relaxator.__init__(self, target_forces)
def run(self, nparal=4):
self.started = True
vj = VaspJob()
kp = KPoints()
vj.initialize(self.structure, self.workdir, kp, binary=self.binary)
grid = None
energies = []
if not self.is_converge:
n = self.initial_number
else:
n = self.convergence_info[-3]['kp_n']
self.convergence_info = []
while True:
density = n ** 3
kp = KPoints.optimized_grid(self.structure.lattice, kp_density=density, force_odd=True)
pcm_log.debug('Trial density: %d Grid: %s' % (density, kp.grid))
if np.sum(grid) != np.sum(kp.grid):
grid = kp.grid
vj.set_kpoints(kp)
vj.clean()
vj.job_static()
vj.input_variables.set_density_for_restart()
vj.input_variables.set_encut(ENCUT=self.encut, POTCAR=self.workdir + os.sep + 'POTCAR')
vj.input_variables.variables['NBANDS'] = nparal * ((30 + self.structure.valence_electrons()) /
nparal + 1)
vj.input_variables.set_ismear(kp)
vj.input_variables.variables['SIGMA'] = 0.2
vj.input_variables.variables['ISPIN'] = 2
vj.set_inputs()
vj.run(use_mpi=True, mpi_num_procs=nparal)
while True:
energy_str = ''
filename = self.workdir + os.sep + 'vasp_stdout.log'
if os.path.exists(filename):
vasp_stdout = read_vasp_stdout(filename=filename)
if len(vasp_stdout['data']) > 2:
scf_energies = [i[2] for i in vasp_stdout['data']]
energy_str = ' %7.3f' % scf_energies[1]
for i in range(1, len(scf_energies)):
if scf_energies[i] < scf_energies[i - 1]:
energy_str += ' >'
else:
energy_str += ' <'
pcm_log.debug(energy_str)
if vj.runner is not None and vj.runner.poll() is not None:
filename = self.workdir + os.sep + 'vasp_stdout.log'
if os.path.exists(filename):
vasp_stdout = read_vasp_stdout(filename=filename)
if len(vasp_stdout['data']) > 2:
scf_energies = [i[2] for i in vasp_stdout['data']]
energy_str += ' %7.3f' % scf_energies[-1]
pcm_log.debug(energy_str)
break
time.sleep(5)
vj.get_outputs()
energy = vj.outcar.final_data['energy']['free_energy']
energies.append(energy)
print('kp_density= %10d kp_grid= %15s free_energy= %9.6f' % (density, grid, energy))
self.convergence_info.append({'free_energy': vj.outcar.final_data['energy']['free_energy'],
'kp_grid': list(grid),
'kp_density': density,
'kp_n': n})
if len(energies) > 2 and abs(max(energies[-3:]) - min(energies[-3:])) < self.energy_tolerance:
self.success = True
break
n += 2
self.output = {'convergence': self.convergence_info, 'best_kp_grid': list(grid)}
self.finished = True
def run(self, nparal=4):
self.started = True
vj = VaspJob(workdir=self.workdir, executable=self.executable)
vj.potcar_setup = self.psp_options
kp = KPoints()
vj.initialize(
structure=self.structure,
kpoints=kp,
pspdir=self.pspdir,
heterostructure=self.heterostructure,
make_potcar=self.make_potcar,
)
grid = None
energies = []
if not self.is_converge:
n = self.initial_number
else:
n = self.convergence_info[-3]["kp_n"]
self.convergence_info = []
while True:
density = n**3
kp = KPoints.optimized_grid(self.structure.lattice,
kp_density=density,
force_odd=True)
pcm_log.debug("Trial density: %d Grid: %s" % (density, kp.grid))
if np.sum(grid) != np.sum(kp.grid):
grid = kp.grid
vj.set_kpoints(kp)
vj.clean()
vj.job_static()
vj.input_variables.set_density_for_restart()
vj.input_variables.set_encut(ENCUT=self.encut,
POTCAR=self.workdir + os.sep +
"POTCAR")
vj.input_variables.variables["NBANDS"] = nparal * (
(30 + self.structure.valence_electrons()) // nparal + 1)
vj.input_variables.set_ismear(kp)
vj.input_variables.variables["SIGMA"] = 0.2
vj.input_variables.variables["ISPIN"] = 2
for i in self.extra_vars:
vj.input_variables[i] = self.extra_vars[i]
vj.set_inputs()
vj.run(mpi_num_procs=nparal)
while True:
energy_str = ""
filename = self.workdir + os.sep + "vasp_stdout.log"
if os.path.exists(filename):
vasp_stdout = read_vasp_stdout(filename=filename)
if len(vasp_stdout["data"]) > 2:
scf_energies = [i[2] for i in vasp_stdout["data"]]
energy_str = " %7.3f" % scf_energies[1]
for i in range(1, len(scf_energies)):
if scf_energies[i] < scf_energies[i - 1]:
energy_str += " >"
else:
energy_str += " <"
pcm_log.debug(energy_str)
if vj.runner is not None and vj.runner.poll() is not None:
filename = self.workdir + os.sep + "vasp_stdout.log"
if os.path.exists(filename):
vasp_stdout = read_vasp_stdout(filename=filename)
if len(vasp_stdout["data"]) > 2:
scf_energies = [
i[2] for i in vasp_stdout["data"]
]
energy_str += " %7.3f" % scf_energies[-1]
pcm_log.debug(energy_str)
break
time.sleep(5)
vj.get_outputs()
energy = (vj.outcar.final_data["energy"]["free_energy"] /
self.structure.natom)
energies.append(energy)
print("kp_density= %10d kp_grid= %15s free_energy= %9.6f" %
(density, grid, energy))
self.convergence_info.append({
"free_energy":
vj.outcar.final_data["energy"]["free_energy"] /
self.structure.natom,
"kp_grid":
list(grid),
"kp_density":
density,
"kp_n":
n,
})
if (len(energies) > 2
and abs(max(energies[-3:]) - min(energies[-3:])) <
self.energy_tolerance):
self.success = True
break
n += 2
self.output = {
"convergence": self.convergence_info,
"best_kp_grid": list(self.best_kpoints.grid),
}
self.finished = True
def __init__(
self,
structure,
workdir=".",
target_forces=1e-3,
executable="vasp",
encut=1.3,
kp_grid=None,
kp_density=1e4,
relax_cell=True,
max_calls=10,
pspdir="potpaw_PBE",
psp_options=None,
extra_vars=None,
heterostructure=False,
make_potcar=True,
auto_ibrion=False,
fire=False,
):
Relaxator.__init__(self, target_forces)
self.target_forces = target_forces
# If heterostructure is true it will keep the repeating order found
# in the POSCAR.
# Added by Uthpala on Apr 20th, 2020.
self.heterostructure = heterostructure
# setting make_potcar=False will not generate a new POTCAR.
# Added by Uthpala on May 12th, 2021.
self.make_potcar = make_potcar
# Turn on adaptive IBRION update.
self.auto_ibrion = auto_ibrion
# Use FIRE algorithm for relaxation.
# Requires VTST Tools
self.fire = fire
self.vaspjob = VaspJob(executable=executable, workdir=workdir)
self.relaxed = False
if kp_grid is not None:
self.kpoints = KPoints(kmode="gamma", grid=kp_grid)
else:
self.kpoints = KPoints.optimized_grid(structure.lattice,
kp_density=kp_density)
self.vaspjob.initialize(
structure=structure,
kpoints=self.kpoints,
pspdir=pspdir,
heterostructure=self.heterostructure,
make_potcar=self.make_potcar,
)
self.vaspjob.potcar_setup = psp_options
self.encut = encut
self.relax_cell = relax_cell
self.max_calls = max_calls
self.pspdir = pspdir
if extra_vars is not None:
self.extra_vars = extra_vars
else:
self.extra_vars = {}
task_params = {
"target_forces": self.target_forces,
"encut": self.encut,
"relax_cell": self.relax_cell,
"max_calls": self.max_calls,
}
Task.__init__(
self,
structure=structure,
task_params=task_params,
workdir=workdir,
executable=executable,
)
def run(self, nparal=4):
self.started = True
vj = VaspJob(workdir=self.workdir, executable=self.executable)
vj.potcar_setup = self.psp_options
kp = KPoints()
vj.initialize(structure=self.structure, kpoints=kp, pspdir=self.pspdir, heterostructure=self.heterostructure)
grid = None
energies = []
if not self.is_converge:
n = self.initial_number
else:
n = self.convergence_info[-3]['kp_n']
self.convergence_info = []
while True:
density = n ** 3
kp = KPoints.optimized_grid(self.structure.lattice, kp_density=density, force_odd=True)
pcm_log.debug('Trial density: %d Grid: %s' % (density, kp.grid))
if np.sum(grid) != np.sum(kp.grid):
grid = kp.grid
vj.set_kpoints(kp)
vj.clean()
vj.job_static()
vj.input_variables.set_density_for_restart()
vj.input_variables.set_encut(ENCUT=self.encut, POTCAR=self.workdir + os.sep + 'POTCAR')
vj.input_variables.variables['NBANDS'] = nparal * ((30 + self.structure.valence_electrons()) //
nparal + 1)
vj.input_variables.set_ismear(kp)
vj.input_variables.variables['SIGMA'] = 0.2
vj.input_variables.variables['ISPIN'] = 2
for i in self.extra_vars:
vj.input_variables[i] = self.extra_vars[i]
vj.set_inputs()
vj.run(mpi_num_procs=nparal)
while True:
energy_str = ''
filename = self.workdir + os.sep + 'vasp_stdout.log'
if os.path.exists(filename):
vasp_stdout = read_vasp_stdout(filename=filename)
if len(vasp_stdout['data']) > 2:
scf_energies = [i[2] for i in vasp_stdout['data']]
energy_str = ' %7.3f' % scf_energies[1]
for i in range(1, len(scf_energies)):
if scf_energies[i] < scf_energies[i - 1]:
energy_str += ' >'
else:
energy_str += ' <'
pcm_log.debug(energy_str)
if vj.runner is not None and vj.runner.poll() is not None:
filename = self.workdir + os.sep + 'vasp_stdout.log'
if os.path.exists(filename):
vasp_stdout = read_vasp_stdout(filename=filename)
if len(vasp_stdout['data']) > 2:
scf_energies = [i[2] for i in vasp_stdout['data']]
energy_str += ' %7.3f' % scf_energies[-1]
pcm_log.debug(energy_str)
break
time.sleep(5)
vj.get_outputs()
energy = vj.outcar.final_data['energy']['free_energy']/self.structure.natom
energies.append(energy)
print('kp_density= %10d kp_grid= %15s free_energy= %9.6f' % (density, grid, energy))
self.convergence_info.append({'free_energy': vj.outcar.final_data['energy']['free_energy']/self.structure.natom,
'kp_grid': list(grid),
'kp_density': density,
'kp_n': n})
if len(energies) > 2 and abs(max(energies[-3:]) - min(energies[-3:])) < self.energy_tolerance:
self.success = True
break
n += 2
self.output = {'convergence': self.convergence_info, 'best_kp_grid': list(self.best_kpoints.grid)}
self.finished = True