def relaxML(self, anew, Fmax=0.1, with_error=True):
a = anew.copy()
# Relax
label = self.traj_namebase + 'ML{}'.format(self.traj_counter)
if with_error:
krr_calc = krr_calculator(self.MLmodel, kappa=self.kappa)
else:
krr_calc = krr_calculator(self.MLmodel)
a_relaxed = relax_VarianceBreak(a, krr_calc, label, niter_max=1, forcemax=Fmax)
return a_relaxed
def update_MLrelaxed_pop(self):
# Initialize MLrelaxed population
self.population.pop_MLrelaxed = []
for a in self.population.pop:
self.population.pop_MLrelaxed.append(a.copy())
E_relaxed_pop = np.zeros(len(self.population.pop))
error_relaxed_pop = np.zeros(len(self.population.pop))
dmin_relaxed_pop = np.zeros(len(self.population.pop))
if self.comm.rank < len(self.population.pop):
index = self.comm.rank
a_MLrelaxed = self.relaxML(self.population.pop[index], Fmax=0.005)
self.population.pop_MLrelaxed[index] = a_MLrelaxed
E_temp, error_temp, _ = self.MLmodel.predict_energy(
a_MLrelaxed, return_error=True)
E_relaxed_pop[index] = E_temp
error_relaxed_pop[index] = error_temp
dmin_temp = self.get_minDistance2data(a_MLrelaxed)
dmin_relaxed_pop[index] = dmin_temp
# test
a_test = a_MLrelaxed.copy()
krr_calc = krr_calculator(self.MLmodel)
a_test.set_calculator(krr_calc)
Ftest = a_test.get_forces(a_MLrelaxed).reshape((-1, 3))
Ftest_max = (Ftest**2).sum(axis=1).max()**0.5
print('Fmax_relaxedPop[{}]'.format(self.comm.rank),
Ftest_max,
flush=True)
for i in range(len(self.population.pop)):
pos = self.population.pop_MLrelaxed[i].positions
self.comm.broadcast(pos, i)
self.population.pop_MLrelaxed[i].set_positions(pos)
E = np.array([E_relaxed_pop[i]])
error = np.array([error_relaxed_pop[i]])
dmin = np.array([dmin_relaxed_pop[i]])
self.comm.broadcast(E, i)
self.comm.broadcast(error, i)
self.comm.broadcast(dmin, i)
self.population.pop_MLrelaxed[i].info['key_value_pairs'][
'predictedEnergy'] = E[0]
self.population.pop_MLrelaxed[i].info['key_value_pairs'][
'predictedError'] = error[0]
self.population.pop_MLrelaxed[i].info['key_value_pairs'][
'fitness'] = E[0] - self.kappa * error[0]
self.population.pop_MLrelaxed[i].info['key_value_pairs'][
'dmin'] = dmin[0]
label_MLrelaxed_pop = self.pop_dir + 'ML_relaxed_pop{}'.format(
self.traj_counter)
write(label_MLrelaxed_pop + '.traj', self.population.pop_MLrelaxed)
label_pop = self.pop_dir + 'pop{}'.format(self.traj_counter)
write(label_pop + '.traj', self.population.pop)
self.comm.barrier()
if self.master:
print('Relaxing population done', flush=True)
def relaxML(self, anew, Fmax=0.1):
a = anew.copy()
# Relax
label = self.traj_namebase + 'ML{}'.format(self.traj_counter)
krr_calc = krr_calculator(self.MLmodel)
a_relaxed = relax_VarianceBreak(a, krr_calc, label, niter_max=1, forcemax=Fmax)
#self.traj_counter += 1
return a_relaxed
