def variance_cost_function(x):
x_sliced=np.split(x,slices[:-1])
for i,k in enumerate(params):
wf.parameters[k]=x_sliced[i].reshape(wf.parameters[k].shape)
wf.recompute(coords)
ke=kinetic(coords,wf)
#Here we assume the ecp is fixed and only recompute
#kinetic energy
En=Enref['total']-Enref['ke']+ke
return np.std(En)**2
def compute_energy(mol, configs, wf, threshold):
ee, ei, ii = self.ewald.energy(configs)
ecp_val = energy.get_ecp(mol, configs, wf, threshold)
ke = energy.kinetic(configs, wf)
return {
"ke": ke,
"ee": ee,
"ei": ei,
"ecp": ecp_val,
"total": ke + ee + ei + ecp_val + ii,
}
def __call__(self, configs, wf):
ee, ei, ii = self.coulomb.energy(configs)
ecp_val = eval_ecp.ecp(self.mol, configs, wf, self.threshold)
ke, grad2 = energy.kinetic(configs, wf)
return {
"ke": ke,
"ee": ee,
"ei": ei,
"ecp": ecp_val,
"grad2": grad2,
"total": ke + ee + ei + ecp_val + ii,
}