{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Instruction\n", "This tutorial explain how to build specific surfaces on the example of (111) surface in FCC copper.\n", "1. Read initial structure\n", "2. Choose new vectors to make required surface normal to one of the vectors\n", "3. Build supercell\n", "4. Build slab with surface\n", "5. Scale slab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import libraries" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import sys\n", "from IPython.display import Image\n", "\n", "from siman import header\n", "from siman.calc_manage import smart_structure_read\n", "from siman.geo import create_supercell, create_surface2, supercell\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Read structure" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "st = smart_structure_read('Cu/POSCARCU.vasp') # read required structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Choose new vectors \n", "The initial structure is FCC lattice in conventianal setting i.e. cubic unit cell.\n", "As a first step we create orthogonal supercell with {111}cub surface on one side. \n", "Below the directions orthogonal to {111} are shown.\n", "We will choose [-1-1-1], [01-1] and [2-1-1]." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='figs/Thompson-tetrahedron-notation-for-FCC-slip-systems.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Build supercell with new vectors" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Old vectors (rprimd):\n", " [[3.6 0. 0. ]\n", " [0. 3.6 0. ]\n", " [0. 0. 3.6]] \n", "New vectors (rprimd) of supercell:\n", " [[-3.6 -3.6 -3.6]\n", " [ 0. 3.6 -3.6]\n", " [ 3.6 -1.8 -1.8]] \n", "The supercell should contain 12.0 atoms ... \n", " -- OK \n", "\n" ] } ], "source": [ "# create supercell using chosen directions, the *mul* allows to choose one half of the third vector\n", "sc = create_supercell(st, [ [-1,-1,-1], [0,1,-1], [2,-1,-1]], mul = (1,1,0.5)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Build slab\n", "Now we need to create vacuum and rotate the cell. This can be done using *create_surface2* function" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- 1 surfaces were generated, choose required surface using *surface_i* argument \n", "\n", "surface position 0.5666666666666667\n", "-- POSCAR was written to /home/aksenov/Simulation_wrapper/siman/tutorials/xyz/POSCAR_CU_vasp_supercell_from_pmg_delcutted \n", "\n", "Final structure contains 28 atoms\n" ] } ], "source": [ "# here we choose [100] normal in supercell, which is equivalent to [111]cub\n", "# combinations of *min_slab_size* and *cut_thickness* (small cut of slab from one side) allows create symmetrical slab\n", "st_suf = create_surface2(sc, [1, 0, 0], min_vacuum_size = 10, \n", " min_slab_size = 16, cut_thickness = 3, oxidation = {'Cu':'Cu0+' }, return_one = 1, surface_i = 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Scale slab \n", "Above the slab with minimum surface area was obtained. If you need larger surface you can use *supercell()* function for which you need to provide required sizes in Angstrems " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- Calculating mul_matrix for ortho: [10, 10, 32] \n", "\n", "mul_matrix_float:\n", " [[ 1.95265238e+00 0.00000000e+00 -2.15902880e-17]\n", " [-3.14010452e-16 2.25472875e+00 -2.15902880e-17]\n", " [ 0.00000000e+00 0.00000000e+00 1.12830771e+00]] \n", "mul_matrix:\n", " [[2 0 0]\n", " [0 2 0]\n", " [0 0 1]] \n", "Old vectors (rprimd):\n", " [[ 5.1 0. 0. ]\n", " [ 0. 4.4 0. ]\n", " [ 0. 0. 28.4]] \n", "New vectors (rprimd) of supercell:\n", " [[10.2 0. 0. ]\n", " [ 0. 8.9 0. ]\n", " [ 0. 0. 28.4]] \n", "The supercell should contain 112.0 atoms ... \n", " -- OK \n", "\n" ] } ], "source": [ "st_sufsc112 = supercell(st_suf, [10,10,32]) # make 2x2 slab" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- POSCAR was written to /home/aksenov/Simulation_wrapper/siman/tutorials/xyz/POSCAR_CU_vasp_supercell_from_pmg_delcutted_supercell \n", "\n" ] }, { "data": { "text/plain": [ "'/home/aksenov/Simulation_wrapper/siman/tutorials/xyz/POSCAR_CU_vasp_supercell_from_pmg_delcutted_supercell'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "st_sufsc112.write_poscar() # save file as POSCAR for VASP" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }