313 lines
7.9 KiB
Plaintext
313 lines
7.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Example 1: Using LAMMPS with PyLammps"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The LAMMPS Python package provides multiple interfaces. The `PyLammps` interface is a high-level abstration of the low-level `lammps` interface. `IPyLammps` further extends this interface with functions that are useful for Jupyter notebooks to enable embedding generated graphics and videos."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Prerequisites\n",
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"\n",
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"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://docs.lammps.org/Python_install.html). There is also a dedicated [PyLammps HowTo](https://docs.lammps.org/Howto_pylammps.html)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Creating a new simulation\n",
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"\n",
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"Once the LAMMPS shared library and the LAMMPS Python package are installed, you can create a new LAMMMPS instance in your Python interpreter as follows:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from lammps import IPyLammps\n",
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"L = IPyLammps()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With `PyLammps`/`IPyLammps` you can write LAMMPS simulations similar to the input script language. Take the following LAMMPS input script:\n",
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"\n",
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"```bash\n",
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"# 3d Lennard-Jones melt\n",
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"\n",
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"units lj\n",
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"atom_style atomic\n",
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"\n",
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"lattice fcc 0.8442\n",
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"region box block 0 4 0 4 0 4\n",
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"create_box 1 box\n",
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"create_atoms 1 box\n",
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"mass 1 1.0\n",
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"\n",
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"velocity all create 1.44 87287 loop geom\n",
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"\n",
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"pair_style lj/cut 2.5\n",
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"pair_coeff 1 1 1.0 1.0 2.5\n",
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"\n",
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"neighbor 0.3 bin\n",
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"neigh_modify delay 0 every 20 check no\n",
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"\n",
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"fix 1 all nve\n",
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"\n",
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"thermo 50\n",
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"```\n",
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"The equivalent can be written with `PyLammps`/`IPyLammps`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 3d Lennard-Jones melt\n",
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"\n",
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"L.units(\"lj\")\n",
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"L.atom_style(\"atomic\")\n",
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"\n",
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"L.lattice(\"fcc\", 0.8442)\n",
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"L.region(\"box\", \"block\", 0, 4, 0, 4, 0, 4)\n",
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"L.create_box(1, \"box\")\n",
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"L.create_atoms(1, \"box\")\n",
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"L.mass(1, 1.0)\n",
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"\n",
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"L.velocity(\"all\", \"create\", 1.44, 87287, \"loop geom\")\n",
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"\n",
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"L.pair_style(\"lj/cut\", 2.5)\n",
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"L.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n",
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"\n",
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"L.neighbor(0.3, \"bin\")\n",
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"L.neigh_modify(\"delay\", 0, \"every\", 20, \"check no\")\n",
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"\n",
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"L.fix(\"1\", \"all\", \"nve\")\n",
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"\n",
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"L.thermo(50)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Visualizing the initial state\n",
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"\n",
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"`IPyLammps` allows you to visualize the current simulation state with the [image](https://docs.lammps.org/Python_module.html#lammps.IPyLammps.image) command. Here we use it to create an image of the initial state of the system."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.image(zoom=1.0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Running simulations\n",
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"\n",
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"Use the `run` command to start the simulation. In Jupyter the return value of the last command will be displayed. The `run` command will return the output of the simulation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.run(150)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can suppress it by adding a semicolon `;`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.run(100);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Visualizing the system will now show us how the atoms have moved."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.image(zoom=1.0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Post-processing thermo output\n",
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"\n",
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"Independent of whether or not you suppress or show the output of the `run` command, `PyLammps` will record the output. Each `run` command creates a new entry in the `L.runs` list. So far our PyLammps instance `L` executed two `run` commands:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"len(L.runs)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Each entry contains information about the simulation run, including the thermo output for the printed out time steps.\n",
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"\n",
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"```bash\n",
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"# thermo output of a LAMMPS simulation run\n",
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"Step Temp E_pair E_mol TotEng Press\n",
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" 0 1.44 -6.7733681 0 -4.6218056 -5.0244179\n",
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" 50 0.70303849 -5.6796164 0 -4.629178 0.50453907\n",
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" 100 0.72628044 -5.7150774 0 -4.6299123 0.29765862\n",
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" 150 0.78441711 -5.805142 0 -4.6331125 -0.086709661\n",
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"```\n",
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"\n",
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"`PyLammps` already parses this information and makes it available as dictionaries and arrays."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.runs[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.runs[1]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For example, the first run was 150 time steps, with printing out a line every 50 steps. You can access the list of time steps using `{entry}.thermo.Step`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.runs[0].thermo.Step"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The corresponding values of each thermo quantity are also accessed this way:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.runs[0].thermo.TotEng"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Together you can use this information to run post-processing on these values or even plot it using `matplotlib`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.xlabel('time step')\n",
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"plt.ylabel('Total Energy')\n",
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"plt.plot(L.runs[0].thermo.Step, L.runs[0].thermo.TotEng)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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