Twisted Graphene Bilayers#
import & prepare#
[1]:
!pip install git+https://github.com/nmdl-mizo/interface_master.git@develop
!mkdir -p cif_files
!curl https://raw.githubusercontent.com/nmdl-mizo/interface_master/develop/test_files/cif_files/C_mp-990448_conventional_standard.cif -o 'cif_files/C_mp-990448_conventional_standard.cif'
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/nmdl-mizo/interface_master.git@develop
Cloning https://github.com/nmdl-mizo/interface_master.git (to revision develop) to /tmp/pip-req-build-c78tkxc7
Running command git clone --filter=blob:none --quiet https://github.com/nmdl-mizo/interface_master.git /tmp/pip-req-build-c78tkxc7
Resolved https://github.com/nmdl-mizo/interface_master.git to commit 2bdf96ed4835b036557ac18de82b4fa2afdabf24
Preparing metadata (setup.py) ... done
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[notice] A new release of pip available: 22.3.1 -> 23.0.1
[notice] To update, run: pip install --upgrade pip
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[2]:
from interfacemaster.cellcalc import get_primitive_hkl, get_pri_vec_inplane, get_normal_index, get_normal_from_MI, rot
from interfacemaster.interface_generator import core, convert_vector_index
from interfacemaster.symmetric_tilt import get_csl_twisted_graphenes
from numpy import array, dot, round, cross, eye, pi, ceil, column_stack, arange
from numpy.linalg import inv, det, norm
from gb_code.csl_generator import print_list
import pandas as pd
Define a get_csl_twisted_graphenes class#
[3]:
#sigmas, rotation angles, CNID areas, num of atoms
sigmas, thetas, A_cnid, anum = get_csl_twisted_graphenes(lim = 10, filename = \
'cif_files/C_mp-990448_conventional_standard.cif',\
maxsigma = 2500, verbose=False)
/home/vscode/.local/lib/python3.10/site-packages/pymatgen/io/cif.py:1169: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
[4]:
df = pd.DataFrame(column_stack((sigmas, thetas/pi*180, A_cnid, anum)), index = arange(len(sigmas))+1, \
columns = ['sigma','thetas','CNID area','atom number'])
df['sigma'] = df['sigma'].astype('int')
df['atom number'] = df['atom number'].astype('int')
df
[4]:
| sigma | thetas | CNID area | atom number | |
|---|---|---|---|---|
| 1 | 7 | 21.786789 | 0.753823 | 28 |
| 2 | 13 | 32.204228 | 0.405905 | 52 |
| 3 | 19 | 13.173551 | 0.277724 | 76 |
| 4 | 31 | 17.896551 | 0.170218 | 124 |
| 5 | 37 | 9.430008 | 0.142615 | 148 |
| 6 | 43 | 15.178179 | 0.122715 | 172 |
| 7 | 49 | 16.426421 | 0.107689 | 196 |
| 8 | 61 | 7.340993 | 0.086504 | 244 |
| 9 | 67 | 35.567302 | 0.078758 | 268 |
| 10 | 73 | 11.635051 | 0.072284 | 292 |
| 11 | 79 | 26.007824 | 0.066794 | 316 |
| 12 | 91 | 6.008983 | 0.057986 | 364 |
| 13 | 97 | 29.409311 | 0.054400 | 388 |
| 14 | 103 | 19.652860 | 0.051231 | 412 |
| 15 | 109 | 10.992733 | 0.048411 | 436 |
| 16 | 127 | 5.085848 | 0.041549 | 508 |
| 17 | 133 | 8.613238 | 0.039675 | 532 |
| 18 | 139 | 34.538944 | 0.037962 | 556 |
| 19 | 151 | 18.733998 | 0.034945 | 604 |
| 20 | 163 | 36.516938 | 0.032373 | 652 |
| 21 | 169 | 4.408455 | 0.031223 | 676 |
| 22 | 181 | 30.158276 | 0.029153 | 724 |
| 23 | 193 | 8.255621 | 0.027341 | 772 |
| 24 | 217 | 3.890238 | 0.024317 | 868 |
| 25 | 223 | 19.274807 | 0.023663 | 892 |
| 26 | 229 | 26.745652 | 0.023043 | 916 |
| 27 | 247 | 14.622221 | 0.021363 | 988 |
| 28 | 259 | 28.783203 | 0.020374 | 1036 |
| 29 | 271 | 3.481006 | 0.019471 | 1084 |
| 30 | 277 | 17.278244 | 0.019050 | 1108 |
| 31 | 283 | 24.016602 | 0.018646 | 1132 |
| 32 | 301 | 6.608610 | 0.017531 | 1204 |
| 33 | 331 | 3.149657 | 0.015942 | 1324 |
| 34 | 337 | 15.654144 | 0.015658 | 1348 |
| 35 | 367 | 11.985100 | 0.014378 | 1468 |
| 36 | 379 | 23.713582 | 0.013923 | 1516 |
| 37 | 403 | 14.307676 | 0.013094 | 1612 |
| 38 | 409 | 19.932013 | 0.012902 | 1636 |
| 39 | 457 | 27.052418 | 0.011547 | 1828 |
| 40 | 481 | 18.365765 | 0.010970 | 1924 |
| 41 | 523 | 20.146584 | 0.010089 | 2092 |
| 42 | 559 | 17.026049 | 0.009440 | 2236 |
| 43 | 613 | 23.301803 | 0.008608 | 2452 |
[5]:
my_interface = core('cif_files/C_mp-990448_conventional_standard.cif',\
'cif_files/C_mp-990448_conventional_standard.cif')
Warning!, this programme will rewrite the POSCAR file in this dir!
/home/vscode/.local/lib/python3.10/site-packages/pymatgen/io/cif.py:1169: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
Searching two_dimensional CSL#
[6]:
#a known target disorientation giving CSL
R = rot(a=[0,0,1],theta = 1.1/180 * pi)
[7]:
my_interface.parse_limit(du = 1e-3, S = 1e-3, sgm1=10000, sgm2=10000, dd = 1e-3)
#Do searching!
my_interface.search_fixed(R=R, exact=False)
Congrates, we found an appx CSL!
U1 =
[[ 0 58 30]
[ 0 28 58]
[ 1 0 0]]; sigma_1 = 2524
U2 =
[[ 0 58 29]
[ 0 29 58]
[ 1 0 0]]; sigma_2 = 2524
D =
[[ 1.00019791e+00 -7.07520000e-04 0.00000000e+00]
[ 7.07520000e-04 1.00019791e+00 0.00000000e+00]
[ 0.00000000e+00 -0.00000000e+00 1.00000000e+00]]
Get bicrystal#
[8]:
hkl = get_primitive_hkl([0, 0, 1], my_interface.conv_lattice_1, my_interface.lattice_1)
my_interface.compute_bicrystal(hkl = hkl, normal_ortho = False, plane_ortho = False, lim = 50, tol_ortho = 1e-2, tol_integer=1e-3)
hkl in CSL: [-1 0 0]
cell 1:
[[ 0 58 -28]
[ 0 28 30]
[ 1 0 0]]
cell 2:
[[ 0 58 -29]
[ 0 29 29]
[ 1 0 0]]
[9]:
layer_displacement = 3.4
my_interface.get_bicrystal(xyz_1 = [1,1,1], xyz_2 = [1,1,1], \
dp1 = 9.99905800e+00 - layer_displacement, filetype= 'VASP')
Sample CNID#
[10]:
gridsize = 0.3
n1 = int(ceil(norm(my_interface.CNID[:,0])/gridsize))
n2 = int(ceil(norm(my_interface.CNID[:,1])/gridsize))
[11]:
import shutil
try:
shutil.rmtree('CNID_inputs')
except:
print('No existing files')
my_interface.sample_CNID([n1,n2], dp1 = 9.99905800e+00 - layer_displacement)
CNID
[[-0.01149425 0.01149425]
[ 0.01149425 0.02298851]
[-0. -0. ]]
making 1 files...
completed
Please check the POSCAR file