01. Quickstart¶
In [1]:
# disable GPU. Remove this if you've compiled HOOMD for GPU
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# import the hoomd, htf packages
import hoomd
import hoomd.htf as htf
import tensorflow as tf
Build the SimModel¶
We prepare the computations that will be executed at each step during the simulation. We can have access to the neighbor list, positions, types, box dimensions of the simulation, but here we only use the neighbor list. Ignore sample_weight for now. Here we define a piece-wise repulsive potential, compute an RDF, and use the auto-differentiation tool to compute forces.
In [2]:
class WCAPotential(htf.SimModel):
def setup(self):
self.avg_rdf = tf.keras.metrics.MeanTensor()
def compute(self, nlist):
# Use Weeks-Chandler-Anderson (WCA) repulisve potential
r12 = htf.nlist_rinv(nlist)**12 # nlist_rinv is neighbor 1 / r^12
# make it so anything above 2^1/6 is 0
r = tf.norm(nlist[:,:,:3], axis=2)
pair_energy = tf.cast(r < 2**(1/6), tf.float32) * r12
particle_energy = tf.reduce_sum(pair_energy, axis=1) # sum over neighbors
forces = htf.compute_nlist_forces(nlist, particle_energy)
# compute rdf
inst_rdf = htf.compute_rdf(nlist, [0, 3.5])
self.avg_rdf.update_state(inst_rdf)
return forces
Running the simulation¶
Now we run the simulation using the usual hoomd-blue syntax. We can specify things like how often the model is called, how often it is saved, etc. in the attach command. This simulation is 144 particles in 2D, whose forces are the ones we defined above.
In [3]:
########### Hoomd-Sim Code ################
hoomd.context.initialize('--mode=cpu')
# this will start TensorFlow, so it goes
# in a with statement for clean exit.
#
# if calling initialize() without params,
# it will be throw error of using unexpected parameter 'f'
# ref: https://github.com/glotzerlab/hoomd-blue/blob/master/hoomd/context.py#L204
L = 16
N = L * L
model = WCAPotential(64)
tfcompute = htf.tfcompute(model)
# create a square lattice
system = hoomd.init.create_lattice(unitcell=hoomd.lattice.sq(a=1.2),
n=[L,L])
nlist = hoomd.md.nlist.cell()
# NVT ensemble with starting temperature of 1
hoomd.md.integrate.mode_standard(dt=0.005)
hoomd.md.integrate.nvt(group=hoomd.group.all(), kT=0.5, tau=0.5).randomize_velocities(seed=1)
tfcompute.attach(nlist, r_cut=5)
#equilibrate
hoomd.run(1000)
# reset rdf statistics
model.avg_rdf.reset_states()
hoomd.run(1000)
HOOMD-blue 2.5.2 DOUBLE HPMC_MIXED TBB SSE SSE2 SSE3 Compiled: 04/30/2019 Copyright (c) 2009-2019 The Regents of the University of Michigan. ----- You are using HOOMD-blue. Please cite the following: * J A Anderson, C D Lorenz, and A Travesset. "General purpose molecular dynamics simulations fully implemented on graphics processing units", Journal of Computational Physics 227 (2008) 5342--5359 * J Glaser, T D Nguyen, J A Anderson, P Liu, F Spiga, J A Millan, D C Morse, and S C Glotzer. "Strong scaling of general-purpose molecular dynamics simulations on GPUs", Computer Physics Communications 192 (2015) 97--107 ----- HOOMD-blue is running on the CPU notice(2): Group "all" created containing 256 particles notice(2): Force mode is FORCE_MODE.tf2hoomd notice(2): Starting TensorflowCompute notice(2): completed reallocate notice(2): Setting flag indicating virial modification will occur notice(2): -- Neighborlist exclusion statistics -- : notice(2): Particles with 0 exclusions : 256 notice(2): Neighbors included by diameter : no notice(2): Neighbors excluded when in the same body: no ** starting run ** Time 00:00:03 | Step 1000 / 1000 | TPS 290.852 | ETA 00:00:00 Average TPS: 290.807 --------- -- Neighborlist stats: 53 normal updates / 10 forced updates / 0 dangerous updates n_neigh_min: 58 / n_neigh_max: 68 / n_neigh_avg: 62.7969 shortest rebuild period: 13 -- Cell list stats: Dimension: 3, 3, 1 n_min : 27 / n_max: 31 / n_avg: 28.4444 ** run complete ** ** starting run ** Time 00:00:05 | Step 2000 / 2000 | TPS 488.064 | ETA 00:00:00 Average TPS: 487.919 --------- -- Neighborlist stats: 52 normal updates / 10 forced updates / 0 dangerous updates n_neigh_min: 58 / n_neigh_max: 66 / n_neigh_avg: 62.5703 shortest rebuild period: 14 -- Cell list stats: Dimension: 3, 3, 1 n_min : 26 / n_max: 32 / n_avg: 28.4444 ** run complete **
Analysis¶
Now we'll plot RDF
In [4]:
import matplotlib.pyplot as plt
import numpy as np
rdf = model.avg_rdf.result().numpy()
plt.plot(rdf[1, :], rdf[0, :] / rdf[0,-1])
Out[4]:
[<matplotlib.lines.Line2D at 0x7fc860081690>]
We can also examine the final positions
In [5]:
pos = tfcompute.get_positions_array()
plt.plot(pos[:,0], pos[:,1], '.')
Out[5]:
[<matplotlib.lines.Line2D at 0x7fc86004fc90>]
