#!/usr/bin/env python
"""
This is adapted from the cubic version
This seems to work for hcp
Things to figure out:
* why multiply strain on right
* why we can't use changing volumes
* why diamond doesn't agree between cubic True and False
"""
import ase
from ase.build import bulk
import numpy as np
import numdifftools as ndt
from ase.calculators.kim.kim import KIM
from scipy.optimize import fmin
import os
import sys
import json
import jinja2
class ElasticConstants(object):
"""Determine the cubic elastic constants by numerically determining the Hessian"""
def __init__(self, calc, element, potentialname, crystalstructure, latticeconstA, latticeconstC):
self.calculator = calc
self.element = element
self.potentialname = potentialname
self.crystalstructure = crystalstructure
# ORIGIN: self.latticeconst = latticeconst
self.latticeconstA = latticeconstA
self.latticeconstC = latticeconstC
self.slab = self.create_slab()
self.o_cell = self.slab.get_cell()
self.slab.set_calculator(self.calculator)
self.o_volume = self.slab.get_volume()
def create_slab(self):
# ORIGIN: slab = bulk(self.element,a=self.latticeconst,crystalstructure=self.crystalstructure,cubic=True)
slab = bulk(self.element,a=self.latticeconstA,c=self.latticeconstC, crystalstructure=self.crystalstructure,cubic=True)
return slab
def voigt_to_matrix(self,voigt_vec):
"""Convert a voigt notation vector to a matrix """
matrix = np.zeros((3,3))
matrix[0,0] = voigt_vec[0]
matrix[1,1] = voigt_vec[1]
matrix[2,2] = voigt_vec[2]
matrix[ tuple([ [1,2], [2,1] ]) ] = voigt_vec[3]
matrix[ tuple([ [0,2], [2,0] ]) ] = voigt_vec[4]
matrix[ tuple([ [0,1], [1,0] ]) ] = voigt_vec[5]
return matrix
def energy_from_strain(self,strain_vec):
""" Apply a strain according to the strain_vec """
# self.slab = self.o_slab.copy()
# print strain_vec
strain_mat = self.voigt_to_matrix(strain_vec)
old_cell = self.o_cell
new_cell = old_cell + np.dot(old_cell, strain_mat)
# new_cell = old_cell + np.einsum('ij,aj->ai',strain_mat,old_cell)
self.slab.set_cell(new_cell,scale_atoms=True)
energy = self.slab.get_potential_energy()
# volume = self.slab.get_volume()
# n_of_atoms = self.slab.get_number_of_atoms()
return (energy/self.o_volume)/ase.units.GPa
def energy_from_scale(self,scale):
# strain_mat = np.eye(3) * (scale)
old_cell = self.o_cell
# new_cell = old_cell + np.dot( strain_mat, old_cell)
new_cell = old_cell * (1 + scale)
self.slab.set_cell(new_cell,scale_atoms=True)
energy = self.slab.get_potential_energy()
# volume = self.slab.get_volume()
# n_of_atoms = self.slab.get_number_of_atoms()
return energy
def results(self):
""" Return the cubic elastic constants """
#get the minimum
self.minscale = float(fmin(self.energy_from_scale,0,xtol=0,ftol=1e-7))
self.oo_cell = self.o_cell.copy()
self.o_cell = self.o_cell * (1+self.minscale)
func = self.energy_from_strain
hess = ndt.Hessian(func, step=0.001, full_output=True)
elastic_constants, info = hess(np.zeros(6, dtype=float))
error_estimate = info.error_estimate
inds11 = tuple([[0,1,2],[0,1,2]])
C11 = elastic_constants[ inds11 ].mean()
C11sig = np.sqrt( ((1./3*error_estimate[ inds11 ])**2).sum() )
inds12 = tuple([[1,2,2,0,0,1],[0,0,1,1,2,2]])
C12 = ( elastic_constants[inds12].mean() )
C12sig = np.sqrt( ((1./6*error_estimate[inds12])**2).sum() )
# ORIGIN
# inds44 = [[3,4,5],[3,4,5]]
# C44 = 1./4 * elastic_constants[inds44].mean()
# C44sig = 1./4 * np.sqrt( ((1./3*error_estimate[inds44])**2).sum() )
B = 1./3 * ( C11 + 2 * C12 )
Bsig = np.sqrt( ( 1./3 * C11sig )**2 + ( 2./3 * C12sig )**2 )
# ORIGIN:
# excessinds = tuple([[3,4,5,3,4,5,3,4,5,0,1,2,4,5,0,1,2,3,5,0,1,2,3,4],
# [0,0,0,1,1,1,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5]])
# excess =(np.abs(elastic_constants[excessinds]).mean() )
# excess_sig = np.sqrt( ((1./24*error_estimate[excessinds])**2).sum() )
results_dict = { 'C11': C11,
'C11_sig' : C11sig,
'C12' : C12,
'C12_sig' : C12sig,
# 'C44': C44,
# 'C44_sig' : C44sig,
'B' : B,
'B_sig' : Bsig,
# 'excess': excess,
# 'excess_sig' : excess_sig,
'units' : 'GPa',
"element": symbol,
"crystal_structure": lattice,
"space_group": space_groups[lattice],
"wyckoff_code": wyckoff_codes[lattice],
# "lattice_constant": self.latticeconst,
"lattice_constant_a": self.latticeconstA,
"lattice_constant_c": self.latticeconstC,
"scale_discrepency": self.minscale,
}
return results_dict
symbol = raw_input()
lattice = raw_input()
model = raw_input()
latticeconst_result_a = raw_input() # ORIGIN: latticeconst_result = raw_input())
latticeconst_result_c = raw_input()
# symbol = 'Fe'
# lattice = 'diamond'
# model = 'EAM_Dynamo_Ackland_Bacon_Fe__MO_142799717516_000'
# latticeconst_result = 2.86652799316e-10
space_groups = {"hcp": "P63/mmc"}
wyckoff_codes = {"hcp": "2d"}
# ORIGIN:
# space_groups = {"fcc": "Fm-3m", "bcc": "Im-3m", "sc": "Pm-3m", "diamond": "Fd-3m"}
# wyckoff_codes = {"fcc": "4a", "bcc": "2a", "sc": "1a", "diamond": "8a"}
normed_basis = {
# ORIGIN: lattice: json.dumps(bulk(symbol, lattice, a=1, cubic=True).positions.tolist(), separators=(' ', ' '))
lattice: json.dumps(bulk(symbol, lattice, a=1, c=1, cubic=True).positions.tolist(), separators=(' ', ' '))
for lattice in space_groups.keys()
}
# ORIGIN: latticeconst = float(latticeconst_result) * 1e10
latticeconstA = float(latticeconst_result_a) * 1e10
latticeconstC = float(latticeconst_result_c) * 1e10
print symbol, lattice, model, latticeconstA, latticeconstC
calc = KIM(model)
# import ase.calculators
# calc = ase.calculators.emt.EMT()
bulkmodulus = ElasticConstants(calc, symbol, model, lattice, latticeconstA, latticeconstC)
results = bulkmodulus.results()
results.update({"basis_coordinates": [
[0, 0, 0],
[2.0/3, 1.0/3, 0.5]
]})
# Repeat species to match normed basis
results.update({"species": '" "'.join([symbol, symbol])})
template_environment = jinja2.Environment(
loader=jinja2.FileSystemLoader('/'),
block_start_string='@[',
block_end_string=']@',
variable_start_string='@<',
variable_end_string='>@',
comment_start_string='@#',
comment_end_string='#@',
undefined=jinja2.StrictUndefined,
)
#template the EDN output
with open(os.path.abspath("output/results.edn"), "w") as f:
template = template_environment.get_template(os.path.abspath("results.edn.tpl"))
f.write(template.render(**results))