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modify_distribution.py
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modify_distribution.py
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#!/usr/bin/env python3
################################################################################
# Raffaele Cheula*[a][b], Matteo Maestri**[a], Giannis Mpourmpakis***[b]
# [a] Politecnico di Milano, [b] University of Pittsburgh
# * raffaele.cheula@polimi.it
# ** matteo.maestri@polimi.it
# *** gmpourmp@pitt.edu
# Modeling Morphology and Catalytic Activity of Nanoparticle Ensembles
# Under Reaction Conditions
# ACS Catalysis 2020
################################################################################
from __future__ import absolute_import, division, print_function
import os, ase, timeit, sys
import numpy as np
import copy as cp
try: import cPickle as pickle
except: import _pickle as pickle
from math import exp, sqrt
from collections import OrderedDict
from ase import Atoms
import matplotlib.pyplot as plt
from nanoparticle_units import *
from nanoparticle_utils import e_relax_from_bond_ols
from active_sites_shells import (get_surface_shell ,
get_fcc_active_shell ,
count_active_sites ,
plot_top_distribution)
################################################################################
# DATA
################################################################################
dirmother = 'particles/'
element = 'Rh'
get_fcc_particles = True
get_dec_particles = True
get_ico_particles = True
n_min_read = 20
n_max_read = 1200
n_min_anal = 20
n_max_anal = 1200
store_particles = True
plot_e_form_n_atoms = False
plot_n_min = 0
plot_n_max = 1200
plot_e_min = 0.30
plot_e_max = 1.30
a_thr = 0.80
b_thr = 0.01
################################################################################
# PARTICLE DATA
################################################################################
e_coh_bulk = -6.1502 # [eV]
m_bond_ols = +2.6800 # [-]
e_twin = +0.0081 # [eV]
shear_modulus = +155 * (giga*Pa)*(J/eV)*(Ang/mt)**3 # [eV/Ang^3]
k_strain_dec = 3.78e-4 # [-]
k_strain_ico = 4.31e-3 # [-]
e_relax_list = e_relax_from_bond_ols(e_coh_bulk = e_coh_bulk,
m_bond_ols = m_bond_ols)
################################################################################
# READ PARTICLES
################################################################################
fcc_particles_dict = {}
dec_particles_dict = {}
ico_particles_dict = {}
step = 10
n_max_per_group = 50
if get_fcc_particles is True:
bulktyp = 'fcc'
dirname = dirmother+bulktyp
for n_group in range(n_min_read, n_max_read, step):
group = '{0:04d}_{1:04d}'.format(n_group, n_group+step)
filename = os.path.join(dirname, '{0}_{1}.pkl'.format(bulktyp, group))
fileobj = open(filename, 'rb')
particles = pickle.load(fileobj)
fileobj.close()
particles = sorted(particles, key = lambda x: x.e_spec)
particles = particles[:n_max_per_group]
fcc_particles_dict[n_group] = particles
if get_dec_particles is True:
bulktyp = 'dec'
dirname = dirmother+bulktyp
for n_group in range(n_min_read, n_max_read, step):
group = '{0:04d}_{1:04d}'.format(n_group, n_group+step)
filename = os.path.join(dirname, '{0}_{1}.pkl'.format(bulktyp, group))
fileobj = open(filename, 'rb')
particles = pickle.load(fileobj)
fileobj.close()
particles = sorted(particles, key = lambda x: x.e_spec)
particles = particles[:n_max_per_group]
dec_particles_dict[n_group] = particles
if get_ico_particles is True:
bulktyp = 'ico'
dirname = dirmother+bulktyp
for n_group in range(n_min_read, n_max_read, step):
group = '{0:04d}_{1:04d}'.format(n_group, n_group+step)
filename = os.path.join(dirname, '{0}_{1}.pkl'.format(bulktyp, group))
fileobj = open(filename, 'rb')
particles = pickle.load(fileobj)
fileobj.close()
particles = sorted(particles, key = lambda x: x.e_spec)
particles = particles[:n_max_per_group]
ico_particles_dict[n_group] = particles
################################################################################
# CREATE PARTICLES
################################################################################
n_iterations = 1
n_atoms_list_fcc = []
e_form_list_fcc = []
e_spec_list_fcc = []
e_spec_min_fcc = {}
if get_fcc_particles is True:
for n_group_zero in range(n_max_anal-step, n_min_anal-step, -step):
print('fcc {:6d}'.format(n_group_zero))
for fcc_particle in fcc_particles_dict[n_group_zero]:
stop = False
while stop is False:
particle = cp.deepcopy(fcc_particle)
particle.remove_atoms(n_iterations = n_iterations,
remove_groups = False )
n_atoms = particle.n_atoms
if n_atoms > n_min_read:
stop = False
n_group = int(np.floor(n_atoms/float(step))*step)
for index in range(len(fcc_particles_dict[n_group])):
part_old = fcc_particles_dict[n_group][index]
if np.array_equal(particle.n_coord_dist,
part_old.n_coord_dist):
stop = True
break
if stop is False:
particle.e_coh_bulk = e_coh_bulk
particle.e_relax_list = e_relax_list
particle.get_energy()
e_thr = -e_coh_bulk*a_thr*n_atoms**(-1./3.)-b_thr
if particle.e_spec > e_thr:
stop = True
else:
fcc_particles_dict[n_group] += [particle]
else:
stop = True
n_atoms_list_dec = []
e_form_list_dec = []
e_spec_list_dec = []
e_spec_min_dec = {}
if get_dec_particles is True:
for n_group_zero in range(n_max_anal-step, n_min_anal-step, -step):
print('dec {:6d}'.format(n_group_zero))
for dec_particle in dec_particles_dict[n_group_zero]:
stop = False
while stop is False:
particle = cp.deepcopy(dec_particle)
particle.remove_atoms(n_iterations = n_iterations,
remove_groups = False )
n_atoms = particle.n_atoms
if n_atoms > n_min_read:
stop = False
n_group = int(np.floor(n_atoms/float(step))*step)
for index in range(len(dec_particles_dict[n_group])):
part_old = dec_particles_dict[n_group][index]
if np.array_equal(particle.n_coord_dist,
part_old.n_coord_dist):
stop = True
break
if stop is False:
particle.e_coh_bulk = e_coh_bulk
particle.e_relax_list = e_relax_list
particle.e_twin = e_twin
particle.shear_modulus = shear_modulus
particle.k_strain = k_strain_dec
particle.get_energy()
e_thr = -e_coh_bulk*a_thr*n_atoms**(-1./3.)-b_thr
if particle.e_spec > e_thr:
stop = True
else:
dec_particles_dict[n_group] += [particle]
else:
stop = True
n_atoms_list_ico = []
e_form_list_ico = []
e_spec_list_ico = []
e_spec_min_ico = {}
if get_ico_particles is True:
for n_group_zero in range(n_max_anal-step, n_min_anal-step, -step):
print('ico {:6d}'.format(n_group_zero))
for ico_particle in ico_particles_dict[n_group_zero]:
stop = False
while stop is False:
particle = cp.deepcopy(ico_particle)
particle.remove_atoms(n_iterations = n_iterations,
remove_groups = False )
n_atoms = particle.n_atoms
if n_atoms > n_min_read:
stop = False
n_group = int(np.floor(n_atoms/float(step))*step)
for index in range(len(ico_particles_dict[n_group])):
part_old = ico_particles_dict[n_group][index]
if np.array_equal(particle.n_coord_dist,
part_old.n_coord_dist):
stop = True
break
if stop is False:
particle.e_coh_bulk = e_coh_bulk
particle.e_relax_list = e_relax_list
particle.e_twin = e_twin
particle.shear_modulus = shear_modulus
particle.k_strain = k_strain_ico
particle.get_energy()
e_thr = -e_coh_bulk*a_thr*n_atoms**(-1./3.)-b_thr
if particle.e_spec > e_thr:
stop = True
else:
ico_particles_dict[n_group] += [particle]
else:
stop = True
################################################################################
# STORE PARTICLES
################################################################################
dirmother = 'particles/'
if store_particles is True:
if get_fcc_particles is True:
bulktyp = 'fcc'
dirname = dirmother+bulktyp
for n_group in range(n_min_read, n_max_read, step):
group = '{0:04d}_{1:04d}'.format(n_group, n_group+step)
filename = os.path.join(dirname, '{0}_{1}.pkl'.format(bulktyp,
group ))
fileobj = open(filename, 'wb')
pickle.dump(fcc_particles_dict[n_group], file = fileobj)
fileobj.close()
if get_dec_particles is True:
bulktyp = 'dec'
dirname = dirmother+bulktyp
for n_group in range(n_min_read, n_max_read, step):
group = '{0:04d}_{1:04d}'.format(n_group, n_group+step)
filename = os.path.join(dirname, '{0}_{1}.pkl'.format(bulktyp,
group ))
fileobj = open(filename, 'wb')
pickle.dump(dec_particles_dict[n_group], file = fileobj)
fileobj.close()
if get_ico_particles is True:
bulktyp = 'ico'
dirname = dirmother+bulktyp
for n_group in range(n_min_read, n_max_read, step):
group = '{0:04d}_{1:04d}'.format(n_group, n_group+step)
filename = os.path.join(dirname, '{0}_{1}.pkl'.format(bulktyp,
group ))
fileobj = open(filename, 'wb')
pickle.dump(ico_particles_dict[n_group], file = fileobj)
fileobj.close()
################################################################################
# FORMATION ENERGY vs N ATOMS PLOT
################################################################################
NC_12 = 0
markersize = 4
width = 0.8
tick_size = 14
label_size = 16
if plot_e_form_n_atoms is True:
n_atoms_list_fcc = []
e_form_list_fcc = []
e_spec_list_fcc = []
if get_fcc_particles is True:
for n_group_zero in range(n_min_read, n_max_read, step):
for particle in fcc_particles_dict[n_group_zero]:
n_atoms_list_fcc += [particle.n_atoms]
e_form_list_fcc += [particle.e_form]
e_spec_list_fcc += [particle.e_spec]
n_atoms_list_dec = []
e_form_list_dec = []
e_spec_list_dec = []
if get_dec_particles is True:
for n_group_zero in range(n_min_read, n_max_read, step):
for particle in dec_particles_dict[n_group_zero]:
n_atoms_list_dec += [particle.n_atoms]
e_form_list_dec += [particle.e_form]
e_spec_list_dec += [particle.e_spec]
n_atoms_list_ico = []
e_form_list_ico = []
e_spec_list_ico = []
if get_ico_particles is True:
for n_group_zero in range(n_min_read, n_max_read, step):
for particle in ico_particles_dict[n_group_zero]:
n_atoms_list_ico += [particle.n_atoms]
e_form_list_ico += [particle.e_form]
e_spec_list_ico += [particle.e_spec]
fig = plt.figure(1)
fig.set_size_inches(16, 10)
if get_fcc_particles is True:
plt.plot(n_atoms_list_fcc, e_spec_list_fcc, marker = 'o', alpha = 0.6,
linestyle = ' ', markersize = markersize,
color = 'darkorange')
plt.plot(e_spec_min_fcc.keys(), e_spec_min_fcc.values(), marker = 'o',
alpha = 0.9, linestyle = ' ', markersize = markersize,
color = 'darkorange')
if get_dec_particles is True:
plt.plot(n_atoms_list_dec, e_spec_list_dec, marker = 'o', alpha = 0.2,
linestyle = ' ', markersize = markersize,
color = 'forestgreen')
plt.plot(e_spec_min_dec.keys(), e_spec_min_dec.values(), marker = 'o',
alpha = 0.6, linestyle = ' ', markersize = markersize,
color = 'forestgreen')
if get_ico_particles is True:
plt.plot(n_atoms_list_ico, e_spec_list_ico, marker = 'o', alpha = 0.2,
linestyle = ' ', markersize = markersize,
color = 'darkviolet')
plt.plot(e_spec_min_ico.keys(), e_spec_min_ico.values(), marker = 'o',
alpha = 0.6, linestyle = ' ', markersize = markersize,
color = 'darkviolet')
plt.axis([plot_n_min, plot_n_max, plot_e_min, plot_e_max])
plt.yticks(fontsize = tick_size)
plt.xticks(fontsize = tick_size)
plt.ylabel('formation energy [eV/atom]', fontsize = label_size)
plt.xlabel('number of atoms', fontsize = label_size)
plt.show()
################################################################################
# END
################################################################################