Commit 4dac1845 by dmar

 { "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from ase.io import read\n", "from ase.visualize import view\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sigma = 3.405\n", "epsilon = 119.8*8.616733e-5 # eV" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def g_step(box, n_bin, d):\n", " box += 1\n", " box *= np.sqrt(2)/sigma\n", " del_bin = box/n_bin #width of a bin\n", " \n", " g = np.zeros([n_bin,n_bin])\n", " g[0] = np.linspace(0,box,n_bin)\n", " g[0]+= del_bin*0.5\n", " \n", " for i in range(len(d)):\n", " for j in range(i+1,len(d)):\n", " g_index = d[i][j]/(del_bin)\n", " g[1,g_index.astype(int)] += 2\n", " # we count both for i and j.\n", " \n", " \n", " #nomalized by number of particles\n", " g[1] /= len(d[0])\n", " #and by bin volume\n", " \n", " for k in range(len(g[1])):\n", " g[1][k] /= ((k+1)**3 -k**3)*del_bin**3\n", " \n", " #side of the optimized cell: 5,269\n", " vol = (5.269/sigma)**3\n", " rho = 4./vol\n", " g[1] /= np.pi*(4/3)*rho\n", " return g;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 38 atom" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trajectory = read('production_38-pos-1.xyz', index='::2')\n", "N = len(trajectory[0]) #number of atoms\n", "n_step = len(trajectory) #number of step\n", "\n", "#create a distance array\n", "dist = np.empty([0,N,N])\n", "for frame in trajectory:\n", " dist = np.append(dist, [frame.get_all_distances()],axis=0)\n", "\n", "#Lennard Jones units\n", "\n", "dist /= sigma" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#understand what the distance array is:\n", "\n", "#dist0 = trajectory[0].get_all_distances()\n", "# print(dist0.shape)\n", "# print(dist0)\n", "# print(dist0[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_bin = 200 #number of bin in the graph\n", "box = 25 #side of the bon in the simulation \n", "g_38 = np.zeros([n_bin,n_bin])\n", "\n", "#average during the thermalized part of the simulation\n", "for i in range(n_step):\n", " g_38 += g_step(box,n_bin,dist[i])\n", "g_38 /= n_step\n", "\n", "plt.plot(g_38[0],g_38[1])\n", "plt.xlim(0,box/(2*sigma))\n", "plt.xlabel('distance [ units of $\\sigma$]')\n", "plt.ylabel('radial distribution function')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 150 atoms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trajectory = read('production_150-pos-1.xyz', index='::2')\n", "N = len(trajectory[0]) #number of atoms\n", "n_step = len(trajectory) #number of step\n", "\n", "dist = np.empty([0,N,N])\n", "for frame in trajectory:\n", " dist = np.append(dist, [frame.get_all_distances()],axis=0)\n", "dist /= sigma\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_bin = 400 \n", "box = 30 \n", "g_150 = np.zeros([n_bin,n_bin])\n", "\n", "\n", "for i in range(n_step):\n", " g_150 += g_step(box,n_bin,dist[i])\n", "g_150 /= n_step\n", "\n", "plt.plot(g_150[0],g_150[1])\n", "plt.xlim(0,box/(2*sigma))\n", "plt.xlabel('distance [ units of $\\sigma$]')\n", "plt.ylabel('radial distribution function')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 450 atoms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trajectory = read('production_450-pos-1.xyz', index='::2')\n", "N = len(trajectory[0]) #number of atoms\n", "n_step = len(trajectory) #number of step\n", "\n", "dist = np.empty([0,N,N])\n", "for frame in trajectory:\n", " dist = np.append(dist, [frame.get_all_distances()],axis=0)\n", "dist /= sigma\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_bin = 400 \n", "box = 40 \n", "g_450 = np.zeros([n_bin,n_bin])\n", "\n", "\n", "for i in range(n_step):\n", " g_450 += g_step(box,n_bin,dist[i])\n", "g_450 /= n_step\n", "\n", "plt.plot(g_450[0],g_450[1])\n", "plt.xlim(0,box/(2*sigma))\n", "plt.xlabel('distance [ units of $\\sigma$]')\n", "plt.ylabel('radial distribution function')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 817 atoms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trajectory = read('production_817-pos-1.xyz', index='::2')\n", "N = len(trajectory[0]) #number of atoms\n", "n_step = len(trajectory) #number of step\n", "\n", "dist = np.empty([0,N,N])\n", "for frame in trajectory:\n", " dist = np.append(dist, [frame.get_all_distances()],axis=0)\n", "dist /= sigma\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_bin = 400 \n", "box = 45 \n", "g_817 = np.zeros([n_bin,n_bin])\n", "\n", "\n", "for i in range(n_step):\n", " g_817 += g_step(box,n_bin,dist[i])\n", "g_817 /= n_step\n", "\n", "plt.plot(g_817[0],g_817[1])\n", "plt.xlim(0,box/(2*sigma))\n", "plt.xlabel('distance [ units of $\\sigma$]')\n", "plt.ylabel('radial distribution function')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### temperature" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Thermalization plots (NVT)\n", "\n", "in the .energ file you can find\n", "\n", "- column 0: step\n", "- column 1: time \n", "- column 2: kinetic energy\n", "- column 3: temperature\n", "- column 4: potential energy\n", "\n", "Try to plot these values and describe what you see and what is conserved" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data38 = np.loadtxt('thermalization_38-1.ener' )\n", "data150 = np.loadtxt('thermalization_150-1.ener')\n", "data450 = np.loadtxt('thermalization_450-1.ener')\n", "data817 = np.loadtxt('thermalization_817-1.ener')\n", "\n", "data38 = np.transpose(data38)\n", "data150 = np.transpose(data150)\n", "data450 = np.transpose(data450)\n", "data817 = np.transpose(data817)\n", "\n", "plt.plot(data38[1],data38[3], label='38')\n", "plt.plot(data150[1],data150[3], label='150')\n", "plt.plot(data450[1],data450[3], label='450')\n", "plt.plot(data817[1],data817[3], label='817')\n", "\n", "plt.legend( loc='upper right')\n", "\n", "plt.xlabel(' time [fs]')\n", "plt.ylabel('T [K]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Production plots (NVE)\n", "\n", "in the .energ file you can find\n", "\n", "- column 0: step\n", "- column 1: time \n", "- column 2: kinetic energy\n", "- column 3: temperature\n", "- column 4: potential energy\n", "\n", "Try to plot these values and describe what you see and what is conserved" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data38 = np.loadtxt('production_38-1.ener' )\n", "data150 = np.loadtxt('production_150-1.ener')\n", "data450 = np.loadtxt('production_450-1.ener')\n", "data817 = np.loadtxt('production_817-1.ener')\n", "\n", "data38 = np.transpose(data38)\n", "data150 = np.transpose(data150)\n", "data450 = np.transpose(data450)\n", "data817 = np.transpose(data817)\n", "\n", "plt.plot(data38[1],data38[3], label='38')\n", "plt.plot(data150[1],data150[3], label='150')\n", "plt.plot(data450[1],data450[3], label='450')\n", "plt.plot(data817[1],data817[3], label='817')\n", "\n", "plt.legend( loc='upper right')\n", "\n", "plt.xlabel(' time [fs]')\n", "plt.ylabel('T [K]')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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", 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 %% Cell type:code id: tags: ` python import numpy as np from ase.io import read from ase.visualize import view