#!/usr/bin/env python """ This seems to work for fcc and bcc and sc 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 Last Update: 2017/08/28 Daniel S. Karls """ from ase.build import bulk from ase.units import GPa import os import numpy as np import numdifftools as ndt from ase.calculators.kim.kim import KIM from scipy.optimize import fmin 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, latticeconst): self.calculator = calc self.element = element self.potentialname = potentialname self.crystalstructure = crystalstructure self.latticeconst = latticeconst 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): slab = bulk(self.element,a=self.latticeconst,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)/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() ) inds44 = tuple([[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 ) 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, "scale_discrepency": self.minscale, } return results_dict symbol = raw_input() lattice = raw_input() model = raw_input() latticeconst_result = raw_input() 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 = { lattice: json.dumps(bulk(symbol, lattice, a=1, cubic=True).positions.tolist(), separators=(' ', ' ')) for lattice in space_groups.keys() } count = bulk(symbol, lattice, a=1, cubic=True).positions.shape[0] # For species latticeconst = float(latticeconst_result) * 1e10 print symbol, lattice, model, latticeconst calc = KIM(model) bulkmodulus = ElasticConstants(calc, symbol, model, lattice, latticeconst) results = bulkmodulus.results() results.update({"basis_coordinates": normed_basis[lattice]}) # Repeat symbols to match normed basis results.update({"species": '" "'.join([symbol] * count)}) 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))