This is a proof-of-concept code that implements PopBD in HOOMD-blue. Key additions are the dynamic bonding and on-the-fly autocorrelation (included here as a submodule) functionalities. Please use this as a jumping off point for your own implementation of PopBD!
NOTE: the latest branch is popbd_cpu, not master. You can merge popbd_cpu into master at your own discretion.
- install instructions
- example run instructions
- suggestions for next steps
See below (or in the original repository) for more HOOMD-blue info:
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 thousands of nodes. For more information, see the HOOMD-blue website.
- Reference Documentation: Full package Python API, usage information, and feature reference.
- Installation Guide: Instructions for installing and compiling HOOMD-blue.
- hoomd-users Google Group: Ask questions to the HOOMD-blue community.
- HOOMD-blue Tutorial: Beginner's guide, code examples, and sample scripts.
- HOOMD-blue website: Additional information, benchmarks, and publications.
HOOMD-blue binaries are available in the glotzerlab-software Docker/Singularity images and for Linux and macOS via the hoomd package on conda-forge. See the Installation Guide for instructions on installing HOOMD-blue or compiling from source.
HOOMD-blue job scripts are Python scripts. You can control system initialization, run protocols, 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 simple cubic lattice of particles with type 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
md.integrate.mode_standard(dt=0.005)
hoomd.md.integrate.langevin(group=hoomd.group.all(), kT=1.2, seed=4)
# Run for 10,000 time steps
hoomd.run(10e3)Save this script as lj.py and run it with python lj.py (or singularity exec software.simg python3 lj.py if using Singularity containers).
See CHANGELOG.rst.
Contributions are welcomed via pull requests. Please report bugs and suggest feature enhancements via the issue tracker. See CONTRIBUTING.md for more information.