Phonon-Vibration-Viewer (For GPUMD)

Visualizing lattice vibration information from phonon dispersion for primitive atoms. ## Introduction * In this tutorial, we will introduce how to obtain the lattice vibration information and visualize it onto atoms by using OVITO. * Visualizing information about lattice vibrations onto the atoms helps to determine whether the atomic vibrations follow a collective mode or are disordered. Further, it is possible to determine whether the phonons at a certain frequency are in propagation mode or localized mode. If one need further information, one can refer to the literatures (Liang 2021, Zhang 2021, Sun 2019, Wang 2021, and Xiong 2016 ). * In this tutorial,I will explain step by step how to visualize the lattice vibrations as follows, using a fivefive twinned diamond nanowire(5FT-DNWs) as an example (Liang 2021). %E5%9B%BE%E7%89%872.jpg

Phonon dispersion

  • Firstly, we need to calculate the phonon dispersion of 5FT-DNWs according the tutorial in GPUMD.

Preparing the Inputs

  • Prepare the POSCAR stucture of 5FT-DNWs. POSCAR file written by Ovito Pro 3.0.0-dev766  1  23.2562 0.0 0.0  0.0 2.51879944 0.0  0.0 0.0 25.23005949  C   155   Direct  0.1879369979 0.4927654661 0.1812349976  0.2814782523 -0.0066748506 0.1825856967  0.3101087086 0.4934641362 0.2639057317  0.2185915403 -0.0070519866 0.201853023  ..............

  • Then, the inputs/outputs for GPUMD are processed using the Atomic Simulation Environment (ASE) and the thermo package.

    Tips: if one is using the nep in GPUMD, the thermo package improved by top Dr. Penghua should be used.

  • Next, importing Relevant Functions of ASE and thermo package.

from pylab import *
from import read
from thermo.gpumd.preproc import add_basis, repeat
from import create_basis, create_kpoints, ase_atoms_to_gpumd
  • Read the POSCAR file and prepare the input files for GPUMD

file_name = 'Fivefold3_POSCAR'
DNWs_unitcell = read(file_name)
DNWs_unitcell.pbc = [True, True, True]
DNWs_unitcell.write("", format='lammps-data')    # Write the unitcell for eigenvector view (see later)
Atoms(symbols='C155', pbc=True, cell=[23.2562, 2.51879944, 25.23005949])
  • Transform unitcell to supercell

DNWs = repeat(DNWs_unitcell, [1, 3, 1])   ## along y-axis
ase_atoms_to_gpumd(DNWs, M=500, cutoff=10)   # output,see GPUMD for how to setup the M and cutoff
  • Write File, the detailed explaination for is available on GPUMD.

  • Write File. The \(k\) vectors are defined in the reciprocal space with respect to the unit cell chosen in the file. We use \(\Gamma - Y\) the path, with 101 \(k\) points in total.

linear_path, sym_points, labels = create_kpoints(DNWs, path='GY', npoints=101)

It is possible that the resulting file is wrong due to a problem with the create_kpoints function in ASE. One has to check the file when calculating the nanowires. The file for the \(\Gamma - Y\) is as follows. 101     0 0 0     0 0.0040967 0     0 0.00819339 0     0 0.0122901 0     0 0.0163868 0     0 0.0204835 0     0 0.0245802 0     0 0.0286769 0     0 0.0327736 0    ..............

  • The file:

    potential       potentials/tersoff/C_Tersoff_1989.txt    0
    minimize         sd       1.0e-10    1000000    # For 5FT-DNWs
    compute_phonon    16.0      0.005                   # In units of A
  • Then, run GPUMD, we can output the omega2.out file for ploting the phonon dispersion.

Plot Phonon Dispersion

  • Set figure properties

def set_fig_properties(ax_list):
    tl = 8
    tw = 2
    tlm = 4

    for ax in ax_list:
        ax.tick_params(which='major', length=tl, width=tw)
        ax.tick_params(which='minor', length=tlm, width=tw)
        ax.tick_params(which='both', axis='both', direction='in', right=True, top=True)
  • Plot figures

The omega2.out output file is loaded and processed to create the following figure. The previously defined kpoints are used for the \(x\)-axis.

# load data
data = np.loadtxt("omega2.out")

for i in range(len(data)):
    for j in range(len(data[0])):

        data[i, j] = np.sqrt(abs(data[i, j])) / (2 * np.pi) * np.sign(data[i, j])

nu = data

# Plot
figure(figsize=(4.5, 6))

# vlines(sym_points, ymin=-0.2, ymax=60, linestyle="--", colors="pink")
# print(nu[0, 4])      # For view

plot(linear_path, nu[:, 0], color='C0', lw=2, label="Tersoff-1989")
plot(linear_path, nu[:, 1:], color='C0', lw=2)
xlim([0, max(linear_path)])
gca().set_xticklabels([r'$\Gamma$', 'Y', 'S', 'X', r'$\Gamma$'])
ylim([0, 20])                                   # or [0, 55] THz
gca().set_yticks(linspace(0, 20, 5))
ylabel(r'$\nu$ (THz)')
legend(frameon=True, loc="best")
  • Since the phonon dispersion in the Liang 2021 article was calculated using the force generated by Lammps to Phonopy. The method of minimization used is different, so the phonon dispersion here is different from the literature. However, the main features are the same. This may also be due to the lack of stability in the structure of 5FT-DNWs.

Eigenvector.out (generate by GPUMD)

With the eigenvector.out file generated by GPUMD, we can obtain the eigenvector at \(\Gamma\) points for their visualization on the atoms.

Please see the GPUMD manual for how to generate eigenvector.out. If you not only want to visualize the eigenvectors of \(\Gamma\) point, you can diagonalize the D.out file by yourself.

  • Tips: One can only modify the file in following way: ``` 1 0 0 0

    ``` Thus, only one kpoint is included to this file. Then, one can rerun the GPUMD to obtain the eigenvector.out.

  • The size ofeigenvector.outfile is small because only the eigenvectors of :math:`Gamma` point are output. Therefore, one can choose a structure with very large atom in unitcell for calculation.

View (generate the lammps dump.file and feed to Ovito)

Run the file

  • Here, we will used the file to process the eigenvector.out file and to generate the dump.file .

  • This dump file contains two frames with the position coordinates of the atoms, the first frame being the original atomic coordinates. The second frame is the atomic coordinates with the eigenvectors (plus). Thus, using ovtio’s Displacement vectors feature, we can visualize the eigenvectors. And with Ovito, we can output great looking images.

  • In the following, we will describe two simple parameters that need to be modified in the

[ ]:
# %load
#!/usr/bin/env python3

@author: LiangTing
2021/12/18 16:06:31
import numpy as np
import os

def get_frequency_eigen_info(num_basis, eig_file='eigenvector.out', directory=None):

    if not directory:
        eig_path = os.path.join(os.getcwd(), eig_file)
        eig_path = os.path.join(directory, eig_file)

    eig_data_file = open(eig_path, 'r')
    data_lines = [line for line in eig_data_file.readlines() if line.strip()]

    om2 = np.array([data_lines[0].split()[0:num_basis * 3]], dtype='float64')
    eigenvector = np.array([data_lines[1 + k].split()[0:num_basis * 3]
                                               for k in range(num_basis * 3)], dtype='float64')

    nu = np.sign(om2) * np.sqrt(abs(np.array(om2))) / (2 * np.pi)

    return nu, eigenvector

def read_from_lammps_structure_data(file_name='lammps-data', units='metal', number_of_dimensions=3):

    # Check file exists
    global column

    if not os.path.isfile(file_name):
        print('LAMMPS data file does not exist!')

    # The column numbers depend by Lammps units
    if units == 'metal':
        column = 5
    elif units == 'real':
        column = 7

    # Read from Lammps data file
    # print("********************* The Structure is Reading *********************")
    lammps_file = open(file_name, 'r')
    data_lines = [line for line in lammps_file.readlines() if line.strip()]

    atom_num_in_box = int(data_lines[1].split()[0])

    direct_cell = np.array([data_lines[i].split()[0:2]
                                 for i in range(3, 3 + number_of_dimensions)], dtype='float64')

    positions_first_frame = np.array([data_lines[7 + k].split()[0:column]
                                           for k in range(atom_num_in_box)], dtype='float64')

    return atom_num_in_box, direct_cell, positions_first_frame

def position_plus_eigen(gamma_freq_points, nu, eigenvector, atom_num_in_box, positions_first_frame):
    import copy

    if atom_num_in_box * 3 != np.size(eigenvector, 1):
        raise ValueError("The data dimension of the eigenvector is inconsistent with atomic number*3")

    print('************* Now the frequency is {0:10.6} THz, the visualization of the eigenvectors is at gamma point'
          '**************** '.format(nu[0][gamma_freq_points]))

    positions_second_frame = copy.deepcopy(positions_first_frame)

    # reshape eigenvector
    eigenvector_x = eigenvector[gamma_freq_points][0:atom_num_in_box]
    eigenvector_y = eigenvector[gamma_freq_points][atom_num_in_box:atom_num_in_box*2]
    eigenvector_z = eigenvector[gamma_freq_points][atom_num_in_box*2:atom_num_in_box*3]

    for i in range(atom_num_in_box):
        positions_second_frame[i][2] = positions_first_frame[i][2] + eigenvector_x[i]  # x
        positions_second_frame[i][3] = positions_first_frame[i][3] + eigenvector_y[i]  # y
        positions_second_frame[i][4] = positions_first_frame[i][4] + eigenvector_z[i]  # z

    return positions_second_frame

def write_to_dump_File(atom_num_in_box, direct_cell, data, fmat, dump_step=1000, file_name='dump_for_visualization.eigen'):

    with open(file_name, fmat) as fid:
        fid.write('ITEM: TIMESTEP\n')
        fid.write('{} \n'.format(dump_step))
        fid.write('ITEM: NUMBER OF ATOMS\n')
        fid.write('ITEM: BOX BOUNDS pp pp pp\n')

        # Boundary
        for i in range(np.size(direct_cell, 0)):
            fid.write('{0:.10f}  {1:20.10f}\n'.format(direct_cell[i][0], direct_cell[i][1]))

        fid.write('ITEM: ATOMS id type x y z\n')
        for i in range(atom_num_in_box):
            fid.write('{0}   {1:.0f} {2:20.10f} {3:20.10f} {4:20.10f}\n'.format(i + 1, data[i][1],

def generate_file(freq, atom_num_in_box, direct_cell, positions_first_frame, positions_second_frame):

    # First frame
    file_name = str(round(freq, 4))+'THz_dump_for_visualization.eigen'
    write_to_dump_File(atom_num_in_box, direct_cell, positions_first_frame, fmat='w', dump_step=1000,

    # second frame
    write_to_dump_File(atom_num_in_box, direct_cell, positions_second_frame, fmat='a', dump_step=2000,

    print('************* dump_for_visualization.eigen is written successfully ************\n')

if __name__ == "__main__":

    num_basis = 155      ## number of atoms in unitcell
    nu, eigenvector = get_frequency_eigen_info(num_basis)
    atom_num_in_box, direct_cell, positions_first_frame = read_from_lammps_structure_data()

    # output
    gamma_freq_points = 4   ## The n-th frequency point on the Gamma point, depending on which frequency point you want to visualize.
    positions_second_frame = position_plus_eigen(gamma_freq_points, nu, eigenvector, atom_num_in_box, positions_first_frame)

    generate_file(nu[0][gamma_freq_points], atom_num_in_box, direct_cell, positions_first_frame, positions_second_frame)

    print('******************** All Done !!! *************************')
  • num_basis = 155

    This means that there are 155 atoms in the unitcell of 5FT-DNWs. We can obain it from POSCAR.

  • gamma_freq_points = 4

    The 4-th frequency point on the \(\Gamma\) point, depending on which frequency point you want to visualize.

************* Now the frequency is    3.37745 THz, the visualization of the eigenvectors is at gamma point****************
************* dump_for_visualization.eigen is written successfully ************

******************** All Done !!! *************************

View the lattice vibration (using the dump file)

Learn to use ovito’s Displacement vectors feature. Eventually, we will get the diagram shown below.