{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 03. EDS biasing using HTF" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the collective variable (CV) being biased is the average distance to center of mass." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# disable GPU. Remove this if you've compiled HOOMD for GPU\n", "import os\n", "os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n", "\n", "import hoomd\n", "import hoomd.md\n", "import hoomd.htf as htf\n", "import tensorflow as tf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the computational graph\n", "\n", "We first create a CV, the distance from the center of mass for each atom. Then we use EDS to bias the CV to match our target value. Note we add the forces ourselves to the compute graph and they depend on the atom positions, not the distance between atoms." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class EDSModel(htf.SimModel):\n", " def setup(self, set_point):\n", " self.cv_avg = tf.keras.metrics.Mean()\n", " self.eds_bias = htf.EDSLayer(set_point, period=25, learning_rate=5.0)\n", "\n", " def compute(self, nlist, positions):\n", " #calculate center of mass\n", " com = tf.reduce_mean(positions[:, :2], 0) \n", " #calculate distance of each atom from center of mass\n", " rs = tf.norm(positions[:, :2] - com, axis=1)\n", " #calculate the average distance from center of mass. This is the collective variable (CV)\n", " real_cv = tf.reduce_mean(rs) \n", " self.cv_avg.update_state(real_cv)\n", " alpha = self.eds_bias(real_cv)\n", " # eds + harmonic bond\n", " energy = real_cv * alpha\n", " forces = htf.compute_positions_forces(positions, energy)\n", " return forces, real_cv, self.cv_avg.result(), alpha\n", "model = EDSModel(0, set_point=4.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running the simulation\n", "\n", "This simulation is 64 LJ particles in an NVT ensemble. We save data every 250 steps." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HOOMD-blue 2.5.2 DOUBLE HPMC_MIXED TBB SSE SSE2 SSE3 \n", "Compiled: 04/30/2019\n", "Copyright (c) 2009-2019 The Regents of the University of Michigan.\n", "-----\n", "You are using HOOMD-blue. Please cite the following:\n", "* J A Anderson, C D Lorenz, and A Travesset. \"General purpose molecular dynamics\n", " simulations fully implemented on graphics processing units\", Journal of\n", " Computational Physics 227 (2008) 5342--5359\n", "* J Glaser, T D Nguyen, J A Anderson, P Liu, F Spiga, J A Millan, D C Morse, and\n", " S C Glotzer. \"Strong scaling of general-purpose molecular dynamics simulations\n", " on GPUs\", Computer Physics Communications 192 (2015) 97--107\n", "-----\n", "HOOMD-blue is running on the CPU\n", "notice(2): Group \"all\" created containing 64 particles\n", "notice(2): -- Neighborlist exclusion statistics -- :\n", "notice(2): Particles with 0 exclusions : 64\n", "notice(2): Neighbors included by diameter : no\n", "notice(2): Neighbors excluded when in the same body: no\n", "** starting run **\n", "Time 00:00:00 | Step 3000 / 3000 | TPS 46431.7 | ETA 00:00:00\n", "Average TPS: 46038.4\n", "---------\n", "-- Neighborlist stats:\n", "103 normal updates / 30 forced updates / 0 dangerous updates\n", "n_neigh_min: 0 / n_neigh_max: 30 / n_neigh_avg: 15.3906\n", "shortest rebuild period: 5\n", "-- Cell list stats:\n", "Dimension: 2, 2, 1\n", "n_min : 10 / n_max: 22 / n_avg: 16\n", "** run complete **\n", "notice(2): Force mode is FORCE_MODE.tf2hoomd \n", "notice(2): Starting TensorflowCompute \n", "notice(2): completed reallocate\n", "notice(2): Setting flag indicating virial modification will occur\n", "** starting run **\n", "Time 00:00:10 | Step 10844 / 18000 | TPS 784.343 | ETA 00:00:09\n", "Time 00:00:19 | Step 18000 / 18000 | TPS 800.52 | ETA 00:00:00\n", "Average TPS: 791.945\n", "---------\n", "-- Neighborlist stats:\n", "1015 normal updates / 150 forced updates / 0 dangerous updates\n", "n_neigh_min: 0 / n_neigh_max: 39 / n_neigh_avg: 21.3438\n", "shortest rebuild period: 9\n", "-- Cell list stats:\n", "Dimension: 2, 2, 1\n", "n_min : 15 / n_max: 17 / n_avg: 16\n", "** run complete **\n" ] } ], "source": [ "#### Hoomd-Sim code ####\n", "\n", "hoomd.context.initialize(\"--mode=cpu\")\n", "tfcompute = htf.tfcompute(model)\n", "#cut off radius: must be less than the box size\n", "rcut = 6.0 \n", "#initialize the lattice\n", "system = hoomd.init.create_lattice(unitcell=hoomd.lattice.sq(a=2.0),n=[8, 8])\n", "nlist = hoomd.md.nlist.cell(check_period=1)\n", "#enable lj pair potential\n", "lj = hoomd.md.pair.lj(rcut, nlist) \n", "#set lj coefficients\n", "lj.pair_coeff.set('A', 'A', epsilon=1.0, sigma=1.0) \n", "hoomd.md.integrate.mode_standard(dt=0.005)\n", "# set up NVT simulation\n", "hoomd.md.integrate.nvt(kT=1.0, tau=0.5,group=hoomd.group.all()) \n", "#equilibrate\n", "hoomd.run(3000)\n", "#simulation\n", "tfcompute.attach(nlist, r_cut=rcut, save_output_period=250)\n", "hoomd.run(15000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis\n", "\n", "Now we plot the CV value and its running average to assess if EDS converged" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np \n", "time = range(0, 15000, 250)\n", "plt.plot(time, tfcompute.outputs[1], label='CV Running Avg', linestyle='--')\n", "plt.plot(time, tfcompute.outputs[0], label='CV', color='C0')\n", "plt.axhline(4.0, label='EDS Set Point', color='C1')\n", "plt.ylabel('CV Value')\n", "plt.xlabel('Time Step')\n", "plt.legend()\n", "plt.show()\n", "plt.plot(time, tfcompute.outputs[2], label='Bias', color='C2')\n", "plt.ylabel('Bias Value')\n", "plt.xlabel('Time Step')\n", "plt.show()" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }