Entropy and enthalpy parameters

In this example, energy and entropy parameters will be used for structural distinction. We will consider bcc, fcc, and hcp structures to calculate the parameters.

import pyscal.core as pc
import pyscal.crystal_structures as pcs
import numpy as np
import matplotlib.pyplot as plt

Now we will create some structures with thermal vibrations

bcc_atoms, bcc_box = pcs.make_crystal('bcc',
                    repetitions=[10,10,10], noise=0.1)
bcc = pc.System()
bcc.atoms = bcc_atoms
bcc.box = bcc_box
fcc_atoms, fcc_box = pcs.make_crystal('fcc',
                     repetitions=[10,10,10], noise=0.1)
fcc = pc.System()
fcc.atoms = fcc_atoms
fcc.box = fcc_box
hcp_atoms, hcp_box = pcs.make_crystal('hcp',
                     repetitions=[10,10,10], noise=0.1)
hcp = pc.System()
hcp.atoms = hcp_atoms
hcp.box = hcp_box

The next step is to calculate the neighbors using adaptive cutoff

bcc.find_neighbors(method='cutoff', cutoff=0)
fcc.find_neighbors(method='cutoff', cutoff=0)
hcp.find_neighbors(method='cutoff', cutoff=0)

In order to calculate energy, a molybdenum potential is used here.

bcc.calculate_energy(species=['Mo'], pair_style='eam/alloy',
                     pair_coeff='* * Mo.set Mo', mass=95,
fcc.calculate_energy(species=['Mo'], pair_style='eam/alloy',
                     pair_coeff='* * Mo.set Mo', mass=95,
hcp.calculate_energy(species=['Mo'], pair_style='eam/alloy',
                     pair_coeff='* * Mo.set Mo', mass=95,

Now we will calculate the entropy parameters

latbcc = (bcc.box[0][1]-bcc.box[0][0])/10
latfcc = (fcc.box[0][1]-fcc.box[0][0])/10
lathcp = (hcp.box[0][1]-hcp.box[0][0])/10
bcc.calculate_entropy(1.4*latbcc, averaged=True, local=True)
fcc.calculate_entropy(1.4*latfcc, averaged=True, local=True)
hcp.calculate_entropy(1.4*lathcp, averaged=True, local=True)

Gather the values

bccenergy = [atom.energy for atom in bcc.atoms]
avgbccenergy = [atom.avg_energy for atom in bcc.atoms]
fccenergy = [atom.energy for atom in fcc.atoms]
avgfccenergy = [atom.avg_energy for atom in fcc.atoms]
hcpenergy = [atom.energy for atom in hcp.atoms]
avghcpenergy = [atom.avg_energy for atom in hcp.atoms]
bccentropy = [atom.entropy for atom in bcc.atoms]
avgbccentropy = [atom.avg_entropy for atom in bcc.atoms]
fccentropy = [atom.entropy for atom in fcc.atoms]
avgfccentropy = [atom.avg_entropy for atom in fcc.atoms]
hcpentropy = [atom.entropy for atom in hcp.atoms]
avghcpentropy = [atom.avg_entropy for atom in hcp.atoms]
plt.scatter(fccenergy, fccentropy, s=10, label='fcc', color='#FFB300')
plt.scatter(hcpenergy, hcpentropy, s=10, label='hcp', color='#388E3C')
plt.scatter(bccenergy, bccentropy, s=10, label='bcc', color='#C62828')
<matplotlib.legend.Legend at 0x7f82fd6bc690>
plt.scatter(avgfccenergy, avgfccentropy, s=10, label='fcc', color='#FFB300')
plt.scatter(avghcpenergy, avghcpentropy, s=10, label='hcp', color='#388E3C')
plt.scatter(avgbccenergy, avgbccentropy, s=10, label='bcc', color='#C62828')
plt.xlabel("Avg Energy")
plt.ylabel("Avg Entropy")
<matplotlib.legend.Legend at 0x7f82fd615d90>