{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating bond orientational order parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example illustrates the calculation of bond orientational order parameters. Bond order parameters, $q_l$ and their averaged versions, $\\bar{q}_l$ are widely used to identify atoms belong to different crystal structures. In this example, we will consider bcc, fcc, and hcp, and calculate the $q_4$ and $q_6$ parameters and their averaged versions which are widely used in literature. More details can be found [here](https://pyscal.readthedocs.io/en/latest/steinhardtparameters.html). " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pyscal as pc\n", "import pyscal.crystal_structures as pcs\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we analyse MD configurations, first a set of perfect bcc, fcc and hcp structures and another set with thermal vibrations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perfect structures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create atoms and box for perfect structures, the :mod:`~pyscal.crystal_structures` module is used. The created atoms and boxes are then assigned to `System` objects." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "bcc_atoms, bcc_box = pcs.make_crystal('bcc', lattice_constant=3.147, repetitions=[4,4,4])\n", "bcc = pc.System()\n", "bcc.box = bcc_box\n", "bcc.atoms = bcc_atoms" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "fcc_atoms, fcc_box = pcs.make_crystal('fcc', lattice_constant=3.147, repetitions=[4,4,4])\n", "fcc = pc.System()\n", "fcc.box = fcc_box\n", "fcc.atoms = fcc_atoms" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "hcp_atoms, hcp_box = pcs.make_crystal('hcp', lattice_constant=3.147, repetitions=[4,4,4])\n", "hcp = pc.System()\n", "hcp.box = hcp_box\n", "hcp.atoms = hcp_atoms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next step is calculation of nearest neighbors. There are two ways to calculate neighbors, by using a cutoff distance or by using the voronoi cells. In this example, we will use the cutoff method and provide a cutoff distance for each structure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Finding the cutoff distance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cutoff distance is normally calculated in a such a way that the atoms within the first shell is incorporated in this distance. The :func:`pyscal.core.System.calculate_rdf` function can be used to find this cutoff distance." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "bccrdf = bcc.calculate_rdf()\n", "fccrdf = fcc.calculate_rdf()\n", "hcprdf = hcp.calculate_rdf()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the calculated rdf is plotted" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(11,4))\n", "ax1.plot(bccrdf[1], bccrdf[0])\n", "ax2.plot(fccrdf[1], fccrdf[0])\n", "ax3.plot(hcprdf[1], hcprdf[0])\n", "ax1.set_xlim(0,5)\n", "ax2.set_xlim(0,5)\n", "ax3.set_xlim(0,5)\n", "ax1.set_title('bcc')\n", "ax2.set_title('fcc')\n", "ax3.set_title('hcp')\n", "ax2.set_xlabel(\"distance\")\n", "ax1.axvline(3.6, color='red')\n", "ax2.axvline(2.7, color='red')\n", "ax3.axvline(3.6, color='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The selected cutoff distances are marked in red in the above plot. For bcc, since the first two shells are close to each other, for this example, we will take the cutoff in such a way that both shells are included." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Steinhardt's parameters - cutoff neighbor method" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "bcc.find_neighbors(method='cutoff', cutoff=3.6)\n", "fcc.find_neighbors(method='cutoff', cutoff=2.7)\n", "hcp.find_neighbors(method='cutoff', cutoff=3.6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have used a cutoff of 3 here, but this is a parameter that has to be tuned. Using a different cutoff for each structure is possible, but it would complicate the method if the system has a mix of structures. Now we can calculate the $q_4$ and $q_6$ distributions " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "bcc.calculate_q([4,6])\n", "fcc.calculate_q([4,6])\n", "hcp.calculate_q([4,6])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thats it! Now lets gather the results and plot them." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "bccq = bcc.get_qvals([4, 6])\n", "fccq = fcc.get_qvals([4, 6])\n", "hcpq = hcp.get_qvals([4, 6])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(bccq[0], bccq[1], s=60, label='bcc', color='#C62828')\n", "plt.scatter(fccq[0], fccq[1], s=60, label='fcc', color='#FFB300')\n", "plt.scatter(hcpq[0], hcpq[1], s=60, label='hcp', color='#388E3C')\n", "plt.xlabel(\"$q_4$\", fontsize=20)\n", "plt.ylabel(\"$q_6$\", fontsize=20)\n", "plt.legend(loc=4, fontsize=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Firstly, we can see that Steinhardt parameter values of all the atoms fall on one specific point which is due to the absence of thermal vibrations. Next, all the points are well separated and show good distinction. However, at finite temperatures, the atomic positions are affected by thermal vibrations and hence show a spread in the distribution. We will show the effect of thermal vibrations in the next example. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Structures with thermal vibrations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, we create the reqd structures using the :mod:`~pyscal.crystal_structures` module. Noise can be applied to atomic positions using the `noise` keyword as shown below." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "bcc_atoms, bcc_box = pcs.make_crystal('bcc', lattice_constant=3.147, repetitions=[10,10,10], noise=0.1)\n", "bcc = pc.System()\n", "bcc.box = bcc_box\n", "bcc.atoms = bcc_atoms" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "fcc_atoms, fcc_box = pcs.make_crystal('fcc', lattice_constant=3.147, repetitions=[10,10,10], noise=0.1)\n", "fcc = pc.System()\n", "fcc.box = fcc_box\n", "fcc.atoms = fcc_atoms" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "hcp_atoms, hcp_box = pcs.make_crystal('hcp', lattice_constant=3.147, repetitions=[10,10,10], noise=0.1)\n", "hcp = pc.System()\n", "hcp.box = hcp_box\n", "hcp.atoms = hcp_atoms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### cutoff method" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "bcc.find_neighbors(method='cutoff', cutoff=3.6)\n", "fcc.find_neighbors(method='cutoff', cutoff=2.7)\n", "hcp.find_neighbors(method='cutoff', cutoff=3.6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now, calculate $q_4$, $q_6$ parameters" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "bcc.calculate_q([4,6])\n", "fcc.calculate_q([4,6])\n", "hcp.calculate_q([4,6])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gather the q vales and plot them" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "bccq = bcc.get_qvals([4, 6])\n", "fccq = fcc.get_qvals([4, 6])\n", "hcpq = hcp.get_qvals([4, 6])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(fccq[0], fccq[1], s=10, label='fcc', color='#FFB300')\n", "plt.scatter(hcpq[0], hcpq[1], s=10, label='hcp', color='#388E3C')\n", "plt.scatter(bccq[0], bccq[1], s=10, label='bcc', color='#C62828')\n", "plt.xlabel(\"$q_4$\", fontsize=20)\n", "plt.ylabel(\"$q_6$\", fontsize=20)\n", "plt.legend(loc=4, fontsize=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The thermal vibrations cause the distributions to spread, but it still very good. Lechner and Dellago proposed using the averaged distributions, $\\bar{q}_4-\\bar{q}_6$ to better distinguish the distributions. Lets try that. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "bcc.calculate_q([4,6], averaged=True)\n", "fcc.calculate_q([4,6], averaged=True)\n", "hcp.calculate_q([4,6], averaged=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "bccaq = bcc.get_qvals([4, 6], averaged=True)\n", "fccaq = fcc.get_qvals([4, 6], averaged=True)\n", "hcpaq = hcp.get_qvals([4, 6], averaged=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets see if these distributions are better.." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(fccaq[0], fccaq[1], s=10, label='fcc', color='#FFB300')\n", "plt.scatter(hcpaq[0], hcpaq[1], s=10, label='hcp', color='#388E3C')\n", "plt.scatter(bccaq[0], bccaq[1], s=10, label='bcc', color='#C62828')\n", "plt.xlabel(\"$q_4$\", fontsize=20)\n", "plt.ylabel(\"$q_6$\", fontsize=20)\n", "plt.legend(loc=4, fontsize=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks much better! We can see that the resolution is much better than the non averaged versions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is also the possibility to calculate structures using Voronoi based neighbor identification too. Let's try that now." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "bcc.find_neighbors(method='voronoi')\n", "fcc.find_neighbors(method='voronoi')\n", "hcp.find_neighbors(method='voronoi')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "bcc.calculate_q([4,6], averaged=True)\n", "fcc.calculate_q([4,6], averaged=True)\n", "hcp.calculate_q([4,6], averaged=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "bccaq = bcc.get_qvals([4, 6], averaged=True)\n", "fccaq = fcc.get_qvals([4, 6], averaged=True)\n", "hcpaq = hcp.get_qvals([4, 6], averaged=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the calculated points.." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(fccaq[0], fccaq[1], s=10, label='fcc', color='#FFB300')\n", "plt.scatter(hcpaq[0], hcpaq[1], s=10, label='hcp', color='#388E3C')\n", "plt.scatter(bccaq[0], bccaq[1], s=10, label='bcc', color='#C62828')\n", "plt.xlabel(\"$q_4$\", fontsize=20)\n", "plt.ylabel(\"$q_6$\", fontsize=20)\n", "plt.legend(loc=4, fontsize=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voronoi based method also provides good resolution,the major difference being that the location of bcc distribution is different." ] } ], "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": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 4 }