# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
import numpy as np
from pyiron.atomistics.job.interactivewrapper import InteractiveWrapper
from pyiron_base import InputList
from pyiron.atomistics.job.interactive import GenericInteractiveOutput
from scipy.optimize import minimize
import scipy
import warnings
__author__ = "Osamu Waseda"
__copyright__ = (
"Copyright 2020, Max-Planck-Institut für Eisenforschung GmbH - "
"Computational Materials Design (CM) Department"
)
__version__ = "1.0"
__maintainer__ = "Osamu Waseda"
__email__ = "waseda@mpie.de"
__status__ = "development"
__date__ = "Sep 1, 2018"
GPa_to_eV_by_A3 = (
1e21 / scipy.constants.physical_constants["joule-electron volt relationship"][0]
)
[docs]class ScipyMinimizer(InteractiveWrapper):
"""
Structure optimization class based on Scipy minimizers.
Example I:
# Position optimization
>>> pr = Project('position')
>>> job = pr.create_job('SomeAtomisticJob', 'atomistic')
>>> job.structure = pr.create_structure('Al', 'fcc', 4.)
>>> # it works also in the static mode, but interactive is recommended
>>> job.server.run_mode.interactive = True
>>> minim = pr.create_job('ScipyMinimizer', 'scipy')
>>> minim.ref_job = job
>>> minim.run()
Example II:
# Volume optimization:
>>> pr = Project('volume')
>>> job = pr.create_job('SomeAtomisticJob', 'atomistic')
>>> job.structure = pr.create_structure('Al', 'fcc', 4.)
>>> # it works also in the static mode, but interactive is recommended
>>> job.server.run_mode.interactive = True
>>> minim = pr.create_job('ScipyMinimizer', 'scipy')
>>> minim.ref_job = job
>>> minim.calc_minimize(pressure=0, volume_only=True)
>>> minim.run()
By setting `volume_only`, positions are not updated, so that only the
pressures are minimized.
It is possible to optimize both the volume and the positions, but since
changing the cell also changes the definition of coordinates, it is a
mathematically ill-defined problem and therefore it might end up in a
physically undefined state. For this reason, it is strongly recommended to
launch several jobs using the Murnaghan class (cf. `Murnaghan`).
In order to perform volume optimization, the child job must have
3x3-pressure output.
"""
def __init__(self, project, job_name):
super(ScipyMinimizer, self).__init__(project, job_name)
self.__name__ = "ScipyMinimizer"
self._ref_job = None
self.input = Input()
self.output = ScipyMinimizerOutput(job=self)
self.interactive_cache = {}
self._delete_existing_job = True
def _initialize_structure(self):
self._original_cell = self.ref_job.structure.cell.copy()
self._current_strain = np.zeros(6)
[docs] def run_static(self):
self.ref_job_initialize()
self._logger.debug("cg status: " + str(self.status))
self._initialize_structure()
if self.ref_job.server.run_mode.interactive:
self._delete_existing_job = False
self.ref_job.run(delete_existing_job=self._delete_existing_job)
self.status.running = True
if self.input.pressure is not None:
x0 = np.zeros(sum(self.input.pressure!=None))
if not self.input.volume_only:
x0 = np.append(x0, self.ref_job.structure.get_scaled_positions().flatten())
else:
x0 = self.ref_job.structure.positions.flatten()
self.output._result = minimize(
method=self.input.minimizer,
fun=self._get_value,
x0=x0,
jac=self._get_gradient,
tol=1.0e-20,
options={'maxiter': self.input.ionic_steps,
'return_all': True }
)
self.status.collect = True
self.collect_output()
if self.ref_job.server.run_mode.interactive:
self.ref_job.interactive_close()
if self["output/convergence"] > 0:
self.status.finished = True
else:
self.status.not_converged = True
@staticmethod
def _tensor_to_voigt(s, strain=False):
ss = 0.5*(s+s.T)
ss = ss.flatten()[[0, 4, 8, 5, 2, 1]]
if strain:
ss[3:] *= 2
return ss
@staticmethod
def _voigt_to_tensor(s, strain=False):
ss = np.array(s).copy()
if not strain:
ss[:3] /= 2
ss = np.array([[ss[0], ss[5], ss[4]], [0, ss[1], ss[3]], [0, 0, ss[2]]])
ss += ss.T
return ss
def _update(self, x):
rerun = False
if self.input.pressure is not None:
if not np.allclose(x[:len(self.input.pressure)], self._current_strain):
if len(self.input.pressure)==1:
self._current_strain[:3] = x[0]
else:
self._current_strain[self.input.pressure!=None] = x[:len(self.input.pressure)]
cell = np.matmul(
self._voigt_to_tensor(self._current_strain, strain=True)+np.eye(3),
self._original_cell
)
self.ref_job.structure.set_cell(cell, scale_atoms=True)
rerun = True
if (not self.input.volume_only
and not np.allclose(
x[len(self.input.pressure):], self.ref_job.structure.get_scaled_positions().flatten()
)):
self.ref_job.structure.set_scaled_positions(x[len(self.input.pressure):].reshape(-1, 3))
rerun = True
elif not np.allclose(x, self.ref_job.structure.positions.flatten()):
self.ref_job.structure.positions = x.reshape(-1, 3)
rerun = True
if rerun:
self.ref_job.run(delete_existing_job=self._delete_existing_job)
[docs] def check_convergence(self):
if self.input.ionic_energy_tolerance > 0:
if len(self.ref_job.output.energy_pot) < 2:
return False
elif np.absolute(np.diff(self.ref_job.output.energy_pot)[-1]) > self.input.ionic_energy_tolerance:
return False
if self.input.ionic_force_tolerance==0:
return True
max_force = np.linalg.norm(self.ref_job.output.forces[-1], axis=-1).max()
if self.input.pressure is None:
if max_force > self.input.ionic_force_tolerance:
return False
elif self.input.volume_only:
if np.absolute(self._get_pressure()-self.input.pressure).max() > self.input.pressure_tolerance:
return False
else:
if max_force > self.input.ionic_force_tolerance:
return False
if np.absolute(self._get_pressure()-self.input.pressure).max() > self.input.pressure_tolerance:
return False
return True
def _get_pressure(self):
if len(self.input.pressure)==1:
return [np.mean(np.diagonal(self.ref_job.output.pressures[-1]))]
else:
return self._tensor_to_voigt(self.ref_job.output.pressures[-1])[self.input.pressure!=None]
def _get_gradient(self, x):
self._update(x)
prefactor = 1.0e-1
if self.check_convergence():
prefactor = 0
if self.input.pressure is not None:
pressure = -(
self._get_pressure()-self.input.pressure
)
if self.input.volume_only:
return pressure*prefactor
else:
return np.append(
pressure,
-np.einsum(
'ij,ni->nj',
np.linalg.inv(self.ref_job.structure.cell),
self.ref_job.output.forces[-1]
).flatten()
).flatten()*prefactor
else:
return -self.ref_job.output.forces[-1].flatten()*prefactor
def _get_value(self, x):
self._update(x)
return self.ref_job.output.energy_pot[-1]
[docs] def collect_output(self):
self.output.to_hdf(self._hdf5)
[docs] def to_hdf(self, hdf=None, group_name=None):
super(ScipyMinimizer, self).to_hdf(hdf=hdf, group_name=group_name)
self.output.to_hdf(self._hdf5)
[docs] def calc_minimize(
self,
max_iter=100,
pressure=None,
algorithm='CG',
ionic_energy_tolerance=0,
ionic_force_tolerance=1.0e-2,
pressure_tolerance=1.0e-3,
volume_only=False,
):
"""
Args:
algorithm (str): scipy algorithm (currently only 'CG' and 'BFGS' run reliably)
pressure (float/list/numpy.ndarray): target pressures
max_iter (int): maximum number of iterations
ionic_energy_tolerance (float): convergence goal in terms of
energy (optional)
ionic_force_tolerance (float): convergence goal in terms of
forces (optional)
volume_only (bool): Only pressure minimization
"""
if pressure is None and volume_only:
raise ValueError('pressure must be specified if volume_only')
if pressure is not None and not volume_only:
warnings.warn(
'Simultaneous optimization of pressures and positions is a'
+ ' mathematically ill posed problem - there is no guarantee'
+ ' that it converges to the desired structure'
)
if pressure is not None:
pressure = np.array([pressure]).flatten()
if len(pressure)==9:
pressure = self._tensor_to_voigt(pressure.reshape(3,3))
if len(pressure)==3:
pressure = np.append(pressure, 3*[None])
self.input.minimizer = algorithm
self.input.ionic_steps = max_iter
self.input.pressure = pressure
self.input.volume_only = volume_only
self.input.ionic_force_tolerance = ionic_force_tolerance
self.input.ionic_energy_tolerance = ionic_energy_tolerance
self.input.pressure_tolerance = pressure_tolerance
[docs]class ScipyMinimizerOutput(GenericInteractiveOutput):
def __init__(self, job):
super(ScipyMinimizerOutput, self).__init__(job=job)
self._result = None
[docs] def to_hdf(self, hdf, group_name="output"):
if self._result is None:
return
with hdf.open(group_name) as hdf_output:
hdf_output["convergence"] = self._result['success']
if 'hess_inv' in self._result.keys():
hdf_output["hessian"] = self._result['hess_inv']