{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 3: 2D circle of particles inside of box with LJ walls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prerequisites\n", "\n", "Before running this example, make sure your Python environment can find the LAMMPS shared library (`liblammps.so`) and the LAMMPS Python package is installed. If you followed the [README](README.md) in this folder, this should already be the case. You can also find more information about how to compile LAMMPS and install the LAMMPS Python package in the [LAMMPS manual](https://lammps.sandia.gov/doc/Python_install.html). There is also a dedicated [PyLammps HowTo](https://lammps.sandia.gov/doc/Howto_pylammps.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup system" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from lammps import IPyLammps" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L = IPyLammps()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 2d circle of particles inside a box with LJ walls\n", "import math\n", "\n", "b = 0\n", "x = 50\n", "y = 20\n", "d = 20\n", "\n", "# careful not to slam into wall too hard\n", "\n", "v = 0.3\n", "w = 0.08\n", " \n", "L.units(\"lj\")\n", "L.dimension(2)\n", "L.atom_style(\"bond\")\n", "L.boundary(\"f f p\")\n", "\n", "L.lattice(\"hex\", 0.85)\n", "L.region(\"box\", \"block\", 0, x, 0, y, -0.5, 0.5)\n", "L.create_box(1, \"box\", \"bond/types\", 1, \"extra/bond/per/atom\", 6)\n", "L.region(\"circle\", \"sphere\", d/2.0+1.0, d/2.0/math.sqrt(3.0)+1, 0.0, d/2.0)\n", "L.create_atoms(1, \"region\", \"circle\")\n", "L.mass(1, 1.0)\n", "\n", "L.velocity(\"all create 0.5 87287 loop geom\")\n", "L.velocity(\"all set\", v, w, 0, \"sum yes\")\n", "\n", "L.pair_style(\"lj/cut\", 2.5)\n", "L.pair_coeff(1, 1, 10.0, 1.0, 2.5)\n", "\n", "L.bond_style(\"harmonic\")\n", "L.bond_coeff(1, 10.0, 1.2)\n", "\n", "L.create_bonds(\"many\", \"all\", \"all\", 1, 1.0, 1.5)\n", "\n", "L.neighbor(0.3, \"bin\")\n", "L.neigh_modify(\"delay\", 0, \"every\", 1, \"check yes\")\n", "\n", "L.fix(1, \"all\", \"nve\")\n", "\n", "L.fix(2, \"all wall/lj93 xlo 0.0 1 1 2.5 xhi\", x, \"1 1 2.5\")\n", "L.fix(3, \"all wall/lj93 ylo 0.0 1 1 2.5 yhi\", y, \"1 1 2.5\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize initial state" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.image(zoom=1.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run simulation and visualize new state" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.thermo_style(\"custom step temp epair press\")\n", "L.thermo(100)\n", "output = L.run(40000)\n", "L.image(zoom=1.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Queries about LAMMPS simulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.system" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.system.natoms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.system.nbonds" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.system.nbondtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.communication" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.fixes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.computes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.dumps" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.groups" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Working with LAMMPS Variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variable(\"a index 2\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variable(\"t equal temp\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "if sys.version_info < (3, 0):\n", " # In Python 2 'print' is a restricted keyword, which is why you have to use the lmp_print function instead.\n", " x = float(L.lmp_print('\"${a}\"'))\n", "else:\n", " # In Python 3 the print function can be redefined.\n", " # x = float(L.print('\"${a}\"')\")\n", " \n", " # To avoid a syntax error in Python 2 executions of this notebook, this line is packed into an eval statement\n", " x = float(eval(\"L.print('\\\"${a}\\\"')\"))\n", "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['t'].value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.eval(\"v_t/2.0\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variable(\"b index a b c\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['b'].value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.eval(\"v_b\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['b'].definition" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variable(\"i loop 10\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['i'].value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.next(\"i\")\n", "L.variables['i'].value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.eval(\"ke\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing Atom data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dir(L.atoms[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].position" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].velocity" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].force" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].type" ] }, { "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", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 1 }