def prod_force_virial(
self, atom_ener: tf.Tensor,
natoms: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
"""
Compute force and virial
Parameters
----------
atom_ener
The atomic energy
natoms
The number of atoms. This tensor has the length of Ntypes + 2
natoms[0]: number of local atoms
natoms[1]: total number of atoms held by this processor
natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
Returns
-------
force
The force on atoms
virial
The total virial
atom_virial
The atomic virial
"""
[net_deriv] = tf.gradients(atom_ener, self.descrpt)
tf.summary.histogram('net_derivative', net_deriv)
net_deriv_reshape = tf.reshape(net_deriv,
[-1, natoms[0] * self.ndescrpt])
force = op_module.prod_force(net_deriv_reshape,
self.descrpt_deriv,
self.nlist,
self.axis,
natoms,
n_a_sel=self.nnei_a,
n_r_sel=self.nnei_r)
virial, atom_virial \
= op_module.prod_virial (net_deriv_reshape,
self.descrpt_deriv,
self.rij,
self.nlist,
self.axis,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
tf.summary.histogram('force', force)
tf.summary.histogram('virial', virial)
tf.summary.histogram('atom_virial', atom_virial)
return force, virial, atom_virial
def comp_ef(self, dcoord, dbox, dtype, tnatoms, name, reuse=None):
t_default_mesh = tf.constant(self.default_mesh)
descrpt, descrpt_deriv, rij, nlist, axis, rot_mat \
= op_module.descrpt (dcoord,
dtype,
tnatoms,
dbox,
t_default_mesh,
self.t_avg,
self.t_std,
rcut_a = self.rcut_a,
rcut_r = self.rcut_r,
sel_a = self.sel_a,
sel_r = self.sel_r,
axis_rule = self.axis_rule)
self.axis = axis
self.nlist = nlist
self.descrpt = descrpt
inputs_reshape = tf.reshape(descrpt, [-1, self.ndescrpt])
atom_ener = self._net(inputs_reshape, name, reuse=reuse)
atom_ener_reshape = tf.reshape(atom_ener, [-1, self.natoms[0]])
energy = tf.reduce_sum(atom_ener_reshape, axis=1)
net_deriv_ = tf.gradients(atom_ener, inputs_reshape)
net_deriv = net_deriv_[0]
net_deriv_reshape = tf.reshape(net_deriv,
[-1, self.natoms[0] * self.ndescrpt])
force = op_module.prod_force(net_deriv_reshape,
descrpt_deriv,
nlist,
axis,
tnatoms,
n_a_sel=self.nnei_a,
n_r_sel=self.nnei_r)
virial, atom_vir = op_module.prod_virial(net_deriv_reshape,
descrpt_deriv,
rij,
nlist,
axis,
tnatoms,
n_a_sel=self.nnei_a,
n_r_sel=self.nnei_r)
return energy, force, virial
def prod_force_virial(self, atom_ener, natoms) :
[net_deriv] = tf.gradients (atom_ener, self.descrpt)
net_deriv_reshape = tf.reshape (net_deriv, [-1, natoms[0] * self.ndescrpt])
force = op_module.prod_force (net_deriv_reshape,
self.descrpt_deriv,
self.nlist,
self.axis,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
virial, atom_virial \
= op_module.prod_virial (net_deriv_reshape,
self.descrpt_deriv,
self.rij,
self.nlist,
self.axis,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
return force, virial, atom_virial
def comp_interpl_ef(self, dcoord, dbox, dtype, tnatoms, name, reuse=None):
descrpt, descrpt_deriv, rij, nlist, axis \
= op_module.descrpt (dcoord,
dtype,
tnatoms,
dbox,
tf.constant(self.default_mesh),
self.t_avg,
self.t_std,
rcut_a = self.rcut_a,
rcut_r = self.rcut_r,
sel_a = self.sel_a,
sel_r = self.sel_r,
axis_rule = self.axis_rule)
inputs_reshape = tf.reshape(descrpt, [-1, self.ndescrpt])
atom_ener = self._net(inputs_reshape, name, reuse=reuse)
sw_lambda, sw_deriv \
= op_module.soft_min_switch(dtype,
rij,
nlist,
tnatoms,
sel_a = self.sel_a,
sel_r = self.sel_r,
alpha = self.smin_alpha,
rmin = self.sw_rmin,
rmax = self.sw_rmax)
inv_sw_lambda = 1.0 - sw_lambda
tab_atom_ener, tab_force, tab_atom_virial \
= op_module.pair_tab(
self.tab_info,
self.tab_data,
dtype,
rij,
nlist,
tnatoms,
sw_lambda,
sel_a = self.sel_a,
sel_r = self.sel_r)
energy_diff = tab_atom_ener - tf.reshape(atom_ener,
[-1, self.natoms[0]])
tab_atom_ener = tf.reshape(sw_lambda, [-1]) * tf.reshape(
tab_atom_ener, [-1])
atom_ener = tf.reshape(inv_sw_lambda, [-1]) * atom_ener
energy_raw = tab_atom_ener + atom_ener
energy_raw = tf.reshape(energy_raw, [-1, self.natoms[0]])
energy = tf.reduce_sum(energy_raw, axis=1)
net_deriv_ = tf.gradients(atom_ener, inputs_reshape)
net_deriv = net_deriv_[0]
net_deriv_reshape = tf.reshape(net_deriv,
[-1, self.natoms[0] * self.ndescrpt])
force = op_module.prod_force(net_deriv_reshape,
descrpt_deriv,
nlist,
axis,
tnatoms,
n_a_sel=self.nnei_a,
n_r_sel=self.nnei_r)
sw_force \
= op_module.soft_min_force(energy_diff,
sw_deriv,
nlist,
tnatoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
force = force + sw_force + tab_force
virial, atom_vir = op_module.prod_virial(net_deriv_reshape,
descrpt_deriv,
rij,
nlist,
axis,
tnatoms,
n_a_sel=self.nnei_a,
n_r_sel=self.nnei_r)
sw_virial, sw_atom_virial \
= op_module.soft_min_virial (energy_diff,
sw_deriv,
rij,
nlist,
tnatoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
# atom_virial = atom_virial + sw_atom_virial + tab_atom_virial
virial = virial + sw_virial \
+ tf.reduce_sum(tf.reshape(tab_atom_virial, [-1, self.natoms[1], 9]), axis = 1)
return energy, force, virial