MoSDeF-GOMC: Python software for the creation of scientific workflows for the Monte Carlo simulation engine GOMC
The GPU Optimized Monte Carlo (GOMC) simulation engine is one of the fastest open-source Monte Carlo engines. Molecular Simulation Design Framework (MoSDeF) software is an open-source software package that efficiently constructs simple or complex chemical systems, applies the force field parameters, and writes several simulation input files. MoSDeF-GOMC uses the power of MoSDeF to build the traditional chemical engineering system, while MoSDeF-GOMC generates all the required files to perform a GOMC simulation.
- The MoSDeF-GOMC software is compatible with the following software or simulation methods, when the files are generated with the Lennard-Jones (LJ) non-bonded potential:
The NAMD molecular dynamics software: The NAMD control file needs to be created by another means, but all the other files (PDB, PSF, and force field files) are compatible.
The py-MCMD software (Hybrid Monte Carlo and molecular dynamics simulations via GOMC and NAMD): All files are compatible except the GOMC control file (PDB, PSF, and force field files) are compatible.
Switching back and forth, in any order or duration, between a GOMC and NAMD simulations, allowing faster equilibrium for GOMC simulations and many other advantages. The newest GOMC versions also retain the atom/bead velocities and pass them back to NAMD.
- MoSDeF-GOMC Highlights:
With tens of lines of MoSDeF-GOMC code, you can generate all the required files to conduct a GOMC simulation (see Simple MoSDeF-GOMC examples).
MoSDeF-GOMC is designed to automate the simulation workflow, allowing high-throughput workflows with GOMC or NAMD, where signac can be utilized to manage the simulations, data storage, and analysis.
MoSDeF-GOMC lowers the barrier of entry for novice users.
MoSDeF-GOMC and MoSDeF permit reproducible simulations, as these automated simulation workflows can build, simulate, and analyze the data in a repeatable manner. This allows the simulation workflows to be easily transferred, replicated, or expanded upon within or outside existing teams.
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