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HOOMD-blue

HOOMD-blue is a general purpose particle simulation toolkit. It performs hard particle Monte Carlo simulations of a variety of shape classes, and molecular dynamics simulations of particles with a range of pair, bond, angle, and other potentials. HOOMD-blue runs fast on NVIDIA GPUs, and can scale across many nodes. For more information, see the HOOMD-blue website.

Tutorial

Read the HOOMD-blue tutorial online.

Installing HOOMD-blue

Official binaries of HOOMD-blue are available via conda through the glotzer channel. To install HOOMD-blue, first download and install miniconda following conda's instructions. Then add the glotzer channel and install HOOMD-blue:

$ conda config --add channels glotzer
$ conda install hoomd

Compiling HOOMD-blue

Use cmake to configure an out of source build and make to build hoomd.

mkdir build
cd build
cmake ../
make -j20

To run out of the build directory, add the build directory to your PYTHONPATH:

export PYTHONPATH=`pwd`:$PYTHONPATH

For more detailed instructions, see the documentation.

Prerequisites

  • Required:
    • Python >= 2.7
    • numpy >= 1.7
    • CMake >= 2.8.0
    • C++ 11 capable compiler (tested with gcc 4.8, 4.9, 5.4, 6.4, clang 3.4, 3.8, 5.0)
  • Optional:
    • NVIDIA CUDA Toolkit >= 7.0
    • MPI (tested with OpenMPI, MVAPICH)
    • sqlite3

Job scripts

HOOMD-blue job scripts are python scripts. You can control system initialization, run protocol, analyze simulation data, or develop complex workflows all with python code in your job.

Here is a simple example.

import hoomd
from hoomd import md
hoomd.context.initialize()

# create a 10x10x10 square lattice of particles with name A
hoomd.init.create_lattice(unitcell=hoomd.lattice.sc(a=2.0, type_name='A'), n=10)
# specify Lennard-Jones interactions between particle pairs
nl = md.nlist.cell()
lj = md.pair.lj(r_cut=3.0, nlist=nl)
lj.pair_coeff.set('A', 'A', epsilon=1.0, sigma=1.0)
# integrate at constant temperature
all = hoomd.group.all();
md.integrate.mode_standard(dt=0.005)
hoomd.md.integrate.langevin(group=all, kT=1.2, seed=4)
# run 10,000 time steps
hoomd.run(10e3)

Save this as lj.py and run with python lj.py.

Reference Documentation

Read the reference documentation on readthedocs.

Change log

See ChangeLog.md.

Contributing to HOOMD-blue.

See CONTRIBUTING.md.