tx258/lammps-gran-kokkos
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

python: update examples and docs

Browse Source
This commit is contained in:
Richard Berger
2024-11-04 09:19:38 -07:00
committed by Richard Berger
parent 9da58b3ffc
commit 24a4ff78b6
31 changed files with 1608 additions and 1730 deletions
Download Patch File Download Diff File Expand all files Collapse all files

351
python/examples/ipython/montecarlo/mc.ipynb Normal file
View File

@ -0,0 +1,351 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Monte Carlo Relaxation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import random, math"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup perfect system"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from lammps import lammps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"L = lammps()\n",
"cmd = L.cmd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cmd.units(\"lj\")\n",
"cmd.atom_style(\"atomic\")\n",
"cmd.atom_modify(\"map array sort\", 0, 0.0)\n",
"\n",
"cmd.dimension(2)\n",
"\n",
"cmd.lattice(\"hex\", 1.0)\n",
"cmd.region(\"box block\", 0, 10, 0, 5, -0.5, 0.5)\n",
"\n",
"cmd.create_box(1, \"box\")\n",
"cmd.create_atoms(1, \"box\")\n",
"cmd.mass(1, 1.0)\n",
"\n",
"cmd.pair_style(\"lj/cut\", 2.5)\n",
"cmd.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n",
"cmd.pair_modify(\"shift\", \"yes\")\n",
"\n",
"cmd.neighbor(0.3, \"bin\")\n",
"cmd.neigh_modify(\"delay\", 0, \"every\", 1, \"check\", \"yes\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"L.ipython.image(zoom=1.6,size=[320,320])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cmd.run(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"emin = L.get_thermo(\"pe\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cmd.dump(\"3 all movie 25 movie.mp4 type type zoom 1.6 adiam 1.0\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Disorder system"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"random.seed(27848)\n",
"deltaperturb = 0.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pos = L.numpy.extract_atom(\"x\")\n",
"for i in range(len(pos)):\n",
" x, y = pos[i][0], pos[i][1]\n",
" dx = deltaperturb * random.uniform(-1, 1)\n",
" dy = deltaperturb * random.uniform(-1, 1)\n",
" pos[i] = (x+dx, y+dy, 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cmd.run(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"L.ipython.image(zoom=1.6,size=[320,320])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Minimize using Monte Carlo moves"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"estart = L.get_thermo(\"pe\")\n",
"elast = estart"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"naccept = 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"energies = [estart]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"niterations = 3000\n",
"deltamove = 0.1\n",
"kT = 0.05"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"natoms = L.extract_global(\"natoms\")\n",
"\n",
"for i in range(niterations):\n",
" pos = L.numpy.extract_atom(\"x\")\n",
" iatom = random.randrange(0, natoms)\n",
" current_atom = pos[iatom]\n",
" \n",
" x0, y0 = current_atom[0], current_atom[1]\n",
" \n",
" dx = deltamove * random.uniform(-1, 1)\n",
" dy = deltamove * random.uniform(-1, 1)\n",
" \n",
" pos[iatom] = (x0+dx, y0+dy, 0)\n",
" \n",
" cmd.run(1, \"pre no post no\")\n",
" \n",
" e = L.get_thermo(\"pe\")\n",
" energies.append(e)\n",
" \n",
" if e <= elast:\n",
" naccept += 1\n",
" elast = e\n",
" elif random.random() <= math.exp(natoms*(elast-e)/kT):\n",
" naccept += 1\n",
" elast = e\n",
" else:\n",
" pos[iatom] = (x0, y0, 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.xlabel('iteration')\n",
"plt.ylabel('potential energy')\n",
"plt.plot(energies)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"L.get_thermo(\"pe\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"emin"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"estart"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"naccept"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"L.ipython.image(zoom=1.6, size=[320,320])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# close dump file to access it\n",
"cmd.undump(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"L.ipython.video(\"movie.mp4\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}