Create LAMMPS Dump File

1. l_Cbpb_Cp: Basis of the 2D lattice in the CSLp reference frame for the given (hkl) plane.

2. l_CRbpb_Cp: Basis of the 2D lattice such that the basis vectors have length that is greater than rCut and the basis vectors are similar in lengths and the angle between them is close to 90. Defined in the CSLp reference frame

import byxtal.tools as gbt
import byxtal.lattice as gbl
import pickle as pkl
import numpy as np
import numpy.linalg as nla
import byxtal.misorient_fz as mfz
import gbpy.generate_hkl_indices as ghi
import gbpy.util_funcs_create_byxtal as uf

########################################################################
l1=gbl.Lattice('Al')
pkl_name = l1.elem_type+'_byxtal_props.pkl'
jar = open(pkl_name, 'rb');

csl_props = pkl.load(jar)
sig_mats = csl_props['sig_mats']
csl_mats = csl_props['csl_mats']
dsc_mats = csl_props['dsc_mats']
bxt_symm_props = csl_props['csl_symm_props']
jar.close()
########################################################################

########################################################################
pkl_name = 'bp_list_' + l1.elem_type + '.pkl'
jar = open(pkl_name, 'rb');

bp_list = pkl.load(jar)
l_p_po = bp_list['l_p_po']
hkl_sig_inds = bp_list['miller_inds']

jar.close()
########################################################################

l_p_po

array([[0.   , 2.025, 2.025],
       [2.025, 0.   , 2.025],
       [2.025, 2.025, 0.   ]])

l1 = gbl.Lattice('Al')
lat_par = l1.lat_params['a']
rCut = lat_par*3
A_cut = (rCut+lat_par)**2

l_p_po = l1.l_p_po
l_po_p = nla.inv(l_p_po)

s1_keys = list(hkl_sig_inds.keys())
ind1 = 0
sig_id = s1_keys[ind1]
hkl_inds = hkl_sig_inds[sig_id]
l_csl_p1 = csl_mats[sig_id]
l_csl_po1 = l_p_po.dot(l_csl_p1)

tct1 = 0
hkl1 = np.zeros((1,3), dtype='int64')
hkl1[0,:] = hkl_inds[tct1,:]

l_csl_po1

array([[ 0.   , -2.025,  4.05 ],
       [ 2.025,  2.025,  4.05 ],
       [-2.025,  0.   ,  4.05 ]])

# for i in range(len(hkl_inds)):
#     print(i)
#     hkl1[0,:] = hkl_inds[i,:]
#     l_CSLbpb_CSLp = {}
#     l_CSLbpbSig_CSLp = {}

#     l_CSLbpb_CSLp_mat = ghi.compute_hkl_bpb(hkl1)
#     l_CSLbpbSig_CSLp_mat = ghi.gen_Acut_bpb(l_CSLbpb_CSLp_mat, l_csl_po1, rCut, A_cut)

#     l_Cbpb_Cp  = l_CSLbpb_CSLp_mat[0]
#     l_CRbpb_Cp = l_CSLbpbSig_CSLp_mat[0]

#     l_bp_po1 = l_csl_po1.dot(l_CRbpb_Cp)

#     symm_grp_ax = bxt_symm_props[sig_id]['symm_grp_ax'];
#     bp_symm_grp = bxt_symm_props[sig_id]['bxt_symm_grp'];

#     l_p2_p1 = sig_mats[sig_id]
#     gb_ID = uf.get_gb_uID(l1, l_bp_po1, l_p2_p1, l_p_po, bp_symm_grp, symm_grp_ax, sig_id)
#     print(gb_ID)

l_CSLbpb_CSLp = {}
l_CSLbpbSig_CSLp = {}

l_CSLbpb_CSLp_mat = ghi.compute_hkl_bpb(hkl1)
l_CSLbpbSig_CSLp_mat = ghi.gen_Acut_bpb(l_CSLbpb_CSLp_mat, l_csl_po1, rCut, A_cut)

l_Cbpb_Cp  = l_CSLbpb_CSLp_mat[0]
l_CRbpb_Cp = l_CSLbpbSig_CSLp_mat[0]

l_bp_po1 = l_csl_po1.dot(l_CRbpb_Cp)

symm_grp_ax = bxt_symm_props[sig_id]['symm_grp_ax'];
bp_symm_grp = bxt_symm_props[sig_id]['bxt_symm_grp'];

l_p2_p1 = sig_mats[sig_id]
gb_ID = uf.get_gb_uID(l1, l_bp_po1, l_p2_p1, l_p_po, bp_symm_grp, symm_grp_ax, sig_id)

gb_ID

'Al_S3_1_N1_1_0_0_N2_-2_1_-2'

zCut = 25*l1.lat_params['a']

threeD_upts, sim_cell2 = uf.create_half_cryst(l1, l_csl_p1, l_CRbpb_Cp, l_p_po, 'upper', zCut)

l_p1_p2 = nla.inv(l_p2_p1)
l_csl_p2 = l_p1_p2.dot(l_csl_p1)
threeD_lpts, sim_cell1 = uf.create_half_cryst(l1, l_csl_p2, l_CRbpb_Cp, l_p_po, 'lower', zCut)

sim_cell2

array([[ 1.43189123e+01, -3.43653896e+00,  0.00000000e+00,
         0.00000000e+00],
       [ 4.44089210e-16,  1.89009643e+01,  0.00000000e+00,
         0.00000000e+00],
       [ 0.00000000e+00,  0.00000000e+00,  2.02500000e+02,
        -1.01250000e+02]])

np.matmul(np.array([-5,7,-2]), np.linalg.inv(l_p2_p1))

array([-7.,  2.,  5.])

l_p2_p1

array([[ 0.66666667, -0.33333333,  0.66666667],
       [ 0.66666667,  0.66666667, -0.33333333],
       [-0.33333333,  0.66666667,  0.66666667]])

l_csl_p2

array([[-1.00000000e+00,  1.11022302e-16,  1.00000000e+00],
       [-1.11022302e-16, -1.00000000e+00,  1.00000000e+00],
       [ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00]])

#test:leila
l_csl_po1 = l_p_po.dot(l_csl_p1)
l_bp_po1 = l_csl_po1.dot(l_CRbpb_Cp)
l_po1_go = uf.compute_orientation(l_bp_po1)

sim_cell1[0:3,0:3]

array([[ 1.43189123e+01, -3.43653896e+00,  0.00000000e+00],
       [ 0.00000000e+00,  1.89009643e+01,  0.00000000e+00],
       [ 8.88178420e-16,  8.88178420e-16,  2.02500000e+02]])

gb_attr = {}
gb_attr['cell'] = sim_cell1
gb_attr['upts'] = threeD_upts
gb_attr['lpts'] = threeD_lpts
gb_attr['lattice'] = l1.elem_type

pkl_name = 'gb_attr_'+gb_ID+'.pkl'
jar = open(pkl_name,'wb')
pkl.dump(gb_attr, jar)
jar.close()

import gbpy.lammps_dump_writer as ldw
initial_dump = 'gb_attr_' + gb_ID + '.out'
box_bound, dump_lamp, box_type = ldw.lammps_box(pkl_name)  # lammps creates from the pkl file
ldw.write_lammps_dump(initial_dump, box_bound, dump_lamp, box_type)

l_csl_p1

array([[ 0.,  1.,  1.],
       [-1., -1.,  1.],
       [ 1.,  0.,  1.]])

l_csl_p2

array([[-1.00000000e+00,  1.11022302e-16,  1.00000000e+00],
       [-1.11022302e-16, -1.00000000e+00,  1.00000000e+00],
       [ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00]])