GPU Optimized Monte Carlo (GOMC)

GOMC is open-source software for simulating molecular systems using the Metropolis Monte Carlo algorithm. The software has been written in object oriented C++, and uses OpenMP and NVIDIA CUDA to allow for execution on multi-core CPU and GPU architectures. GOMC employs widely-used simulation file types (PDB, PSF, CHARMM-style parameter files) .

GOMC can be used to study vapor–liquid equilibria, adsorption in porous materials, surfactant self-assembly, and condensed phase structure for complex molecules. To learn more about GOMC, please refer to our documentation and recently published GOMC paper.

Ensembles

GOMC supports simulations in a variety of ensembles, which include: 

  • Canonical
  • Isothermal–isobaric
  • Grand canonical
  • Gibbs ensemble (constant volume and pressure)

Force fields

GOMC supports a variety of all-atom, united atom, and coarse grained force fields such as:

  • OPLS
  • CHARMM
  • TraPPE
  • Mie
  • Martini

Molecule

GOMC supports a variety of molecular topologies:  

Monte Carlo moves

GOMC supports a variety of Monte Carlo moves, such as:

Announcements

  • Version 2.40 released on 5/9/2019
  • Version 2.31 released on 5/21/2018
  • Version 2.30 released on 5/10/2018

Future Release

Multiparticle MC move

In the next release, GOMC will support Multiparticle MC move, where all molecules will displace and/or rotate simultaneously along the force. If you are interested to use this feature, click on this image to navigate to our GitHub repository.

Parallel Tempering

In the next release, GOMC will support the parallel tempering in NVT, NPT, GCMC, and GEMC simulation. In GOMC, each replica can be run in parallel, using OpenMP. If you are interested to use this feature, click on this image to navigate to our GitHub repository.

Free Energy

In the Next release, GOMC will support free energy calculation in NVT and NPT ensemble, using TI, BAR, and MBAR methods. Post analysis of GOMC energy output can be done using alchemlyb python library, developed at Arizona State University. If you are interested to use this feature, click on this image to navigate to our GitHub repository.

Buckingham potential

In the next release, GOMC will support the Buckingham potential, viral, and long range correction. If you are interested to use this feature, click on this image to navigate to our GitHub repository.

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