def launch_opt_jobs(self):
"""
Mimicing DihedralScanner.launch_opt_jobs,
"""
assert hasattr(self, 'next_jobs') and hasattr(
self, 'current_finished_job_results')
while len(self.opt_queue) > 0:
m, from_grid_id, to_grid_id = self.opt_queue.pop()
# check if this job already exists
m_geo_key = get_geo_key(m.xyzs[0])
if m_geo_key in self.task_cache[to_grid_id]:
final_geo, final_energy, final_gradient, job_folder = self.task_cache[
to_grid_id][m_geo_key]
result_m = Molecule()
result_m.elem = list(m.elem)
result_m.xyzs = [final_geo]
result_m.qm_energies = [final_energy]
if final_gradient is not None:
result_m.qm_grads = [final_gradient]
result_m.build_topology()
grid_id = self.get_dihedral_id(result_m,
check_grid_id=to_grid_id)
if grid_id is None:
print(
f"Cached result from {job_folder} is ignored because optimized geometry is far from grid id {to_grid_id}"
)
else:
self.current_finished_job_results.push((result_m, grid_id),
priority=job_folder)
else:
# append the job to self.next_jobs, which is the output of torsiondrive-API
self.next_jobs[to_grid_id].append(m.xyzs[0].copy())
def launch_opt_jobs(self):
"""
Launch constrained optimizations for molecules in opt_queue
Tasks current opt_queue will be popped in order.
If a task exist in self.task_cache, the cached result will be checked, then put into self.current_finished_job_results
Else, the task will be launched by self.launch_constrained_opt, and information is saved as
self.running_job_path_info[job_path] = m, from_grid_id, to_grid_id
"""
assert hasattr(self, 'running_job_path_info') and hasattr(
self, 'current_finished_job_results')
while len(self.opt_queue) > 0:
m, from_grid_id, to_grid_id = self.opt_queue.pop()
# check if this job already exists
m_geo_key = get_geo_key(m.xyzs[0])
if m_geo_key in self.task_cache[to_grid_id]:
final_geo, final_energy, final_gradient, job_folder = self.task_cache[
to_grid_id][m_geo_key]
result_m = Molecule()
result_m.elem = list(m.elem)
result_m.xyzs = [final_geo]
result_m.qm_energies = [final_energy]
if final_gradient is not None:
result_m.qm_grads = [final_gradient]
result_m.build_topology()
grid_id = self.get_dihedral_id(result_m,
check_grid_id=to_grid_id)
if grid_id is None:
print(
f"Cached result from {job_folder} is ignored because optimized geometry is far from grid id {to_grid_id}"
)
else:
self.current_finished_job_results.push((result_m, grid_id),
priority=job_folder)
#self.grid_status[to_grid_id].append((m.xyzs[0], final_geo, final_energy))
else:
job_path = self.launch_constrained_opt(m, to_grid_id)
self.running_job_path_info[
job_path] = m, from_grid_id, to_grid_id
def finish(self):
""" Write qdata.txt and scan.xyz file based on converged scan results """
m = Molecule()
m.elem = list(self.engine.M.elem)
m.qm_energies, m.xyzs, m.comms = [], [], []
# optionally writing qm gradients into qdata.txt if avilable
writing_gradients = False
if len(self.grid_final_gradients) == len(self.grid_final_geometries):
m.qm_grads = []
writing_gradients = True
# only print grid with energies
for gid in sorted(self.grid_energies.keys()):
m.qm_energies.append(self.grid_energies[gid])
m.xyzs.append(self.grid_final_geometries[gid])
if writing_gradients:
m.qm_grads.append(self.grid_final_gradients[gid])
m.comms.append("Dihedral %s Energy %.9f" %
(str(gid), self.grid_energies[gid]))
m.write('qdata.txt')
print(
f"Final scan energies{' and gradients' if writing_gradients else ''} are written to qdata.txt"
)
m.write('scan.xyz')
print("Final scan energies are written to scan.xyz")
