def update(self, cluster: Cluster, step: int) -> None:
super().update(cluster=cluster, step=step)
cluster.minimum = False
cluster = self.database.insert_cluster(cluster)
worker_pool = Pool(processes=self.n_processes)
minima = Pool.map(self.quench(cluster),
[cluster for i in range(self.n_quench)])
for _ in range(self.n_quench):
minimum = self.quench(cluster)
minimum.step = step
minimum = self.database.insert_cluster(minimum)
minima.append(minimum)
self.results_dict[cluster] = Counter(minima)
def minimize(self, cluster: Cluster) -> Cluster:
self.step_size = self.initial_step
step = 0
_ = cluster.get_particle_positions()
# n_coords = len(_[1])
self.coords = _[0]
np.random.shuffle(self.coords)
last_energy = self.pot.get_energy(cluster)
converged = False
while step <= self.max_steps and not converged:
converged = True
step += 1
for idx in range(len(self.coords)):
self._take_step(self.coords[idx])
cluster.set_particle_positions((self.coords, _[1], _[2]))
new_energy = self.pot.get_energy(cluster)
# print(new_energy, last_energy)
dE = new_energy - last_energy
last_energy = new_energy
self.update()
if abs(dE) >= self.convergence_gradient:
converged = False
# print(f"Exiting in {step} steps")
# for i in self.coords:
# print("Cl " + " ".join([str(a) for a in i]))
cluster.minimum = True
return cluster