def predict(self, confs, return_std=False, ncores=1):
""" Predict the forces acting on the central atoms of confs using the
2- and 3-body GPs. The total force is the sum of the two predictions.
Args:
confs (list): List of M x 5 arrays containing coordinates and
atomic numbers of atoms within a cutoff from the central one
return_std (bool): if True, returns the standard deviation
associated to predictions according to the GP framework
Returns:
forces (array): array of force vectors predicted by the GPs
forces_errors (array): errors associated to the force predictions,
returned only if return_std is True
"""
if return_std:
if self.rep_sig:
rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
force_2b, std_2b = self.gp_2b.predict(confs, return_std)
force_2b += rep_forces
else:
force_2b, std_2b = self.gp_2b.predict(
confs, return_std, ncores=ncores)
force_3b, std_3b = self.gp_2b.predict(
confs, return_std, ncores=ncores)
return force_2b + force_3b, std_2b + std_3b
else:
if self.rep_sig:
rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
return self.gp_2b.predict(confs, return_std, ncores=ncores) + rep_forces + \
self.gp_3b.predict(confs, return_std, ncores=ncores)
else:
return self.gp_2b.predict(confs, return_std, ncores=ncores) + \
self.gp_3b.predict(confs, return_std, ncores=ncores)
def fit_force_and_energy(self, confs, forces, glob_confs, energies, ncores=1):
""" Fit the GP to a set of training energies using a 2- and
3-body single species force-force, energy-energy, and energy-forces kernel
functions. The 2-body Gaussian process is first fitted, then the 3-body GP
is fitted to the difference between the training energies (and forces) and
the 2-body predictions of energies (and forces) on the training configurations.
Args:
confs (list): List of M x 5 arrays containing coordinates and
atomic numbers of atoms within a cutoff from the central one
forces (array) : Array containing the vector forces on
the central atoms of the training configurations
glob_confs (list of lists): List of configurations arranged so that
grouped configurations belong to the same snapshot
energies (array) : Array containing the total energy of each snapshot
ncores (int): number of CPUs to use for the gram matrix evaluation
"""
hypotetical_model_name = "models/MODEL_ker_TwoBodySingleSpecies_ntr_%i.json" %(len(energies)+len(forces))
try:
model_2b = models.TwoBodySingleSpeciesModel.from_json(hypotetical_model_name)
self.rep_sig = model_2b.rep_sig
self.gp_2b = model_2b.gp
if self.rep_sig:
self.rep_energies = utility.get_repulsive_energies(
glob_confs, self.rep_sig)
energies -= self.rep_energies
self.rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
forces -= self.rep_forces
print("Loaded 2-body model to bootstart training")
except:
if self.rep_sig:
self.rep_sig = utility.find_repulstion_sigma(confs)
self.rep_energies = utility.get_repulsive_energies(
glob_confs, self.rep_sig)
energies -= self.rep_energies
self.rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
forces -= self.rep_forces
self.gp_2b.fit_force_and_energy(
confs, forces, glob_confs, energies, ncores=ncores)
two_body_forces = self.gp_2b.predict(confs, ncores=ncores)
two_body_energies = self.gp_2b.predict_energy(
glob_confs, ncores=ncores)
self.gp_3b.fit_force_and_energy(
confs, forces - two_body_forces, glob_confs, energies - two_body_energies, ncores=ncores)
def fit_force_and_energy(self,
confs,
forces,
glob_confs,
energies,
ncores=1):
""" Fit the GP to a set of training forces and energies using
2-body single species force-force, energy-force and energy-energy kernels
Args:
confs (list): List of M x 5 arrays containing coordinates and
atomic numbers of atoms within a cutoff from the central one
forces (array) : Array containing the vector forces on
the central atoms of the training configurations
glob_confs (list of lists): List of configurations arranged so that
grouped configurations belong to the same snapshot
energies (array) : Array containing the total energy of each snapshot
ncores (int): number of CPUs to use for the gram matrix evaluation
"""
if self.rep_sig:
self.rep_sig = utility.find_repulstion_sigma(confs)
self.rep_energies = utility.get_repulsive_energies(
glob_confs, self.rep_sig)
energies -= self.rep_energies
self.rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
forces -= self.rep_forces
self.gp.fit_force_and_energy(confs,
forces,
glob_confs,
energies,
ncores=ncores)
def fit(self, confs, forces, ncores=1):
""" Fit the GP to a set of training forces using a 2- and
3-body single species force-force kernel functions. The 2-body Gaussian
process is first fitted, then the 3-body GP is fitted to the difference
between the training forces and the 2-body predictions of force on the
training configurations
Args:
confs (list): List of M x 5 arrays containing coordinates and
atomic numbers of atoms within a cutoff from the central one
forces (array) : Array containing the vector forces on
the central atoms of the training configurations
ncores (int): number of CPUs to use for the gram matrix evaluation
"""
hypotetical_model_name = "models/MODEL_ker_TwoBodyManySpecies_ntr_%i.json" %(len(forces))
try:
model_2b = models.TwoBodyManySpeciesModel.from_json(hypotetical_model_name)
self.rep_sig = model_2b.rep_sig
self.gp_2b = model_2b.gp
if self.rep_sig:
self.rep_sig = utility.find_repulstion_sigma(confs)
self.rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
forces -= self.rep_forces
print("Loaded 2-body model to bootstart training")
except:
if self.rep_sig:
self.rep_sig = utility.find_repulstion_sigma(confs)
self.rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
forces -= self.rep_forces
self.gp_2b.fit(confs, forces, ncores=ncores)
ntr = len(confs)
two_body_forces = self.gp_2b.predict(confs, ncores=ncores)
self.gp_3b.fit(confs, forces - two_body_forces, ncores=ncores)
def fit(self, confs, forces, ncores=1):
""" Fit the GP to a set of training forces using a
2-body single species force-force kernel
Args:
confs (list): List of M x 5 arrays containing coordinates and
atomic numbers of atoms within a cutoff from the central one
forces (array) : Array containing the vector forces on
the central atoms of the training configurations
ncores (int): number of CPUs to use for the gram matrix evaluation
"""
if self.rep_sig:
self.rep_sig = utility.find_repulstion_sigma(confs)
self.rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
forces -= self.rep_forces
self.gp.fit(confs, forces, ncores=ncores)
def predict(self, confs, return_std=False, ncores=1):
""" Predict the forces acting on the central atoms of confs using a GP
Args:
confs (list): List of M x 5 arrays containing coordinates and
atomic numbers of atoms within a cutoff from the central one
return_std (bool): if True, returns the standard deviation
associated to predictions according to the GP framework
Returns:
forces (array): array of force vectors predicted by the GP
forces_errors (array): errors associated to the force predictions,
returned only if return_std is True
"""
if self.rep_sig:
rep_forces = utility.get_repulsive_forces(confs, self.rep_sig)
return self.gp.predict(confs, return_std,
ncores=ncores) + rep_forces
else:
return self.gp.predict(confs, return_std, ncores=ncores)
