md_relax_job.structure = gt_gs
md_relax_job.forcefield = ezff.generate_forcefield(template, rr, FFtype = 'reaxff')
md_relax_job.options['pbc'] = False
md_relax_job.options['relax_atoms'] = True
md_relax_job.options['relax_cell'] = False
md_relax_job.options['atomic_charges'] = True
# Run GULP calculation
md_relax_job.run(command='gulp')
# Read output from completed GULP job and clean-up
md_relax = md_relax_job.read_structure()
md_relax_job.cleanup()
# Calculate error
charg_error = ezff.error_atomic_charges(MD=md_gs_atomic_charges, GT=gt_gs_atomic_charges)
disp_error = ezff.error_structure_distortion(MD=md_relax, GT=gt_gs)
return [charg_error, disp_error]
# Generate forcefield template and variable ranges
FF = reax_forcefield('ffield')
FF.make_template_qeq('S')
FF.generate_templates()
# Read template and variable ranges
bounds = ezff.read_variable_bounds('param_ranges', verbose=False)
template = ezff.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 = 16, mutation_probability = 0.4)
ezff.optimize(problem, algorithm, iterations = 4)
import ezff
from ezff.interfaces import gulp, vasp
import numpy as np
bounds = ezff.read_variable_bounds('variable_bounds', verbose=False)
template = ezff.read_forcefield_template('template')
# DEFINE GROUND TRUTHS
gt_bulk_modulus = 1.1236 #GPa
gt_structure = vasp.read_atomic_structure('ground_truths/POSCAR')
def my_error_function(variable_values):
# Get rank from pool
try:
myrank = pool.rank
except:
myrank = 0
# Configure GULP job.
path = str(myrank)
md_job = gulp.job(path=path)
md_job.structure = gt_structure
md_job.forcefield = ezff.generate_forcefield(template,
variable_values,
FFtype='SW')
md_job.options['pbc'] = True
md_job.options['relax_atoms'] = True
md_job.options['relax_cell'] = True
# Submit job and read output