def compute(self, nlist, positions, box, sample_weight):
# get r
r = htf.safe_norm(tensor=nlist[:, :, :3], axis=2)
p_energy = self.lj(r)
energy = tf.reduce_sum(input_tensor=p_energy, axis=1)
forces = htf.compute_nlist_forces(nlist, energy)
return forces
def mol_compute(self, nlist, positions, mol_nlist, mol_positions, box):
# assume particle (w) is 0
r = tf.norm(mol_nlist, axis=3)
rinv = tf.math.divide_no_nan(1.0, r)
mol_p_energy = 4.0 / 2.0 * (rinv**12 - rinv**6)
total_e = tf.reduce_sum(input_tensor=mol_p_energy)
forces = htf.compute_nlist_forces(nlist, total_e)
return forces
def compute(self, nlist, positions, box, sample_weight):
# get r
rinv = htf.nlist_rinv(nlist)
inv_r6 = rinv**6
# pairwise energy. Double count -> divide by 2
p_energy = 4.0 / 2.0 * (inv_r6 * inv_r6 - inv_r6)
# sum over pairwise energy
energy = tf.reduce_sum(input_tensor=p_energy, axis=1)
forces = htf.compute_nlist_forces(nlist, energy)
return forces
def compute(self, nlist, positions, box, sample_weight):
# get r
r = tf.norm(tensor=nlist[:, :, :3], axis=2)
# compute 1 / r while safely treating r = 0.
# pairwise energy. Double count -> divide by 2
inv_r6 = tf.math.divide_no_nan(1., r**6)
p_energy = 4.0 / 2.0 * (inv_r6 * inv_r6 - inv_r6)
# sum over pairwise energy
energy = tf.reduce_sum(input_tensor=p_energy, axis=1)
tf.print(energy)
forces = htf.compute_nlist_forces(nlist, energy)
return forces
def compute(self, nlist, positions, box, sample_weight):
rinv = htf.nlist_rinv(nlist)
# closest neighbors have largest value in 1/r, take top
top_n = tf.sort(rinv, axis=1, direction='DESCENDING')[
:, :self.top_neighs]
# run through NN
# make sure shape is definite
top_n = tf.reshape(top_n, (-1, self.top_neighs))
x = self.dense1(top_n)
x = self.dense2(x)
energy = self.last(x)
forces = htf.compute_nlist_forces(nlist, energy)
return forces
def compute(self, nlist, positions, box, sample_weight):
# get r
r = tf.norm(tensor=nlist[:, :, :3], axis=2)
# compute 1 / r while safely treating r = 0.
# pairwise energy. Double count -> divide by 2
inv_r6 = tf.math.divide_no_nan(1., r**6)
p_energy = 4.0 / 2.0 * (inv_r6 * inv_r6 - inv_r6)
# get rdf
rdf, rs = htf.compute_rdf(nlist, [3, 5], positions[:, 3])
# also compute without types
_, _ = htf.compute_rdf(nlist, [3, 5])
# compute running mean
self.avg_rdf.update_state(rdf, sample_weight=sample_weight)
forces = htf.compute_nlist_forces(nlist, p_energy)
return forces
def compute(self, nlist, positions, training):
rinv = htf.nlist_rinv(nlist)
# closest neighbors have largest value in 1/r, take top
top_n = tf.sort(rinv, axis=1, direction='DESCENDING')[
:, :self.top_neighs]
# run through NN
x = self.dense1(top_n)
x = self.dense2(x)
energy = self.last(x)
if training:
energy *= 2
forces = htf.compute_nlist_forces(nlist, energy)
if self.output_zero:
energy *= 0.
return forces, tf.reduce_sum(energy)
def compute(self, nlist):
rinv = htf.nlist_rinv(nlist)
energy = rinv
forces = htf.compute_nlist_forces(nlist, energy)
return forces
def compute(self, nlist):
r = htf.safe_norm(nlist[:, :3], axis=2)
rbf = self.rbf(r)
energy = tf.reduce_sum(self.dense(rbf))
forces = htf.compute_nlist_forces(nlist, energy)
return forces
def compute(self, nlist):
energy = self.wca(nlist)
forces = htf.compute_nlist_forces(nlist, energy)
return forces