phon_error_relaxed = ezff.error_phonon_dispersion(MD=md_relax_disp_GS,
GT=gt_relax_disp_GS,
weights='acoustic')
phon_error_expanded = ezff.error_phonon_dispersion(MD=md_expanded_disp_GS,
GT=gt_expanded_disp_GS,
weights='acoustic')
phon_error_compressed = ezff.error_phonon_dispersion(
MD=md_compressed_disp_GS, GT=gt_compressed_disp_GS, weights='acoustic')
return [
a_lattice_error, b_lattice_error, modulus_error, phon_error_relaxed,
phon_error_compressed, phon_error_expanded
]
pool = ezff.Pool()
problem = ezff.OptProblem(num_errors=6,
variable_bounds=bounds,
error_function=my_error_function,
template=template)
algo1 = ezff.Algorithm(problem, 'NSGAIII', population=64, pool=pool)
iter1 = 10
algo2 = ezff.Algorithm(problem, 'NSGAII', population=128, pool=pool)
iter2 = 5
algo3 = ezff.Algorithm(problem, 'IBEA', population=32, pool=pool)
iter3 = 10
algorithms = [algo1, algo2, algo3]
iterations = [iter1, iter2, iter3]
ezff.optimize(problem, algorithms, iterations)
pool.close()
expanded_job.options['relax_atoms'] = True
expanded_job.options['relax_cell'] = False
expanded_job.options['phonon_dispersion'] = True
# Submit job and read output
expanded_job.options['phonon_dispersion_from'] = '0 0 0'
expanded_job.options['phonon_dispersion_to'] = '0.5 0.5 0'
expanded_job.write_script_file()
expanded_job.run(command='gulp')
md_expanded_disp_GS = gulp.read_phonon_dispersion(expanded_job.path +
'/out.gulp.disp')
phon_error_expanded = ezff.error_phonon_dispersion(md_expanded_disp_GS,
gt_expanded_disp_GS,
weights='acoustic')
expanded_job.cleanup() # FINISH EXPANDED JOB
return [
a_latt_error, b_latt_error, modulus_error, phon_error_relax,
phon_error_compressed, phon_error_expanded
]
pool = ezff.Pool()
problem = ezff.OptProblem(num_errors=6,
variable_bounds=bounds,
error_function=my_error_function,
template=template)
algorithm = ezff.Algorithm(problem, 'NSGAII', population=128, pool=pool)
ezff.optimize(problem, algorithm, iterations=10)
pool.close()
# Calculate error
md_dis_scan_energies = np.array(md_dis_scan_energies)
gt_dis_scan_energies = np.array(gt_dis_scan_energies)
md_gs_energy = np.array(md_gs_energy)
gt_gs_energy = np.array(gt_gs_energy)
dis_error = ezff.error_PES_scan( md_dis_scan_energies-md_gs_energy, gt_dis_scan_energies-gt_gs_energy, weights = 'dissociation')
return [freq_error, dis_error]
pool = ezff.Pool()
if pool.is_master():
# Generate forcefield template and variable ranges
FF = reax_forcefield('ffield')
FF.make_template_twobody('S','C',double_bond=True)
FF.make_template_threebody('S','C','S')
FF.make_template_fourbody('S','C','S','S')
FF.generate_templates()
time.sleep(5.0)
# Read template and variable ranges
bounds = ffio.read_variable_bounds('param_ranges', verbose=False)
template = ffio.read_forcefield_template('ff.template.generated')
problem = ezff.OptProblem(num_errors = 2, variable_bounds = bounds, error_function = my_error_function, template = template)
algorithm = ezff.Algorithm(problem, 'NSGAII', population = 128, pool = pool)
ezff.optimize(problem, algorithm, iterations = 4, write_forcefields = 5)
pool.close()