(103j) py-MCMD: Hybrid Monte Carlo – Molecular Dynamics Simulations

Hybrid simulations utilize the unique strengths of Monte Carlo and molecular dynamics simulations to optimize the sampling of phase space, and enable simulations that are not possible with either simulation type on its own [1, 2, 3, 4]. In this work, we present the py-MCMD software, which allows Monte Carlo and molecular dynamics simulations to work together as a hybrid simulation. py-MCMD manages simulation execution and the data transfer between the simulation engines. GPU Optimized Monte Carlo (GOMC) [5, 6] is used for Monte Carlo moves, while NAMD [7, 8] is used for molecular dynamics sampling.

To validate the workflow, and highlight some of its capabilities, hybrid MC/MD simulations are performed for SPC/E water in the isobaric-isothermal (NPT), and grand canonical (GC) ensembles, as well as with Gibbs ensemble Monte Carlo (GEMC). The hybrid MC/MD approach agrees with reference MC simulations within the statistical uncertainty of the calculations, and has a computational efficiency that is 2 to 136 times greater than traditional Monte Carlo simulations. The influence of the integrator parameters, and parameters controlling the calculation of electrostatic energies, on the accuracy of the results, and the efficiency of the simulation, are discussed. MC/MD simulations performed for water in a graphene slit pore illustrate significant gains in sampling efficiency when using the coupled--decoupled configurational-bias (CD-CBMC) algorithm[9] compared to unbiased single random trial insertions. Simulations using CD-CBMC reach equilibrium 25 times faster than simulations using unbiased single random trial insertions. In a more challenging application, hybrid grand canonical Monte Carlo/molecular dynamics (GC MC/MD) simulations are used to hydrate a buried binding pocket in the bovine pancreatic trypsin inhibitor (BPTI). Water occupancies produced by GC MC/MD simulations are in close agreement with crystallographically identified positions. GC MC/MD simulations have a computational efficiency that is 5 times better than MD simulations. py-MCMD is available on GitHub at https://github.com/GOMC-WSU/py-MCMD.

References

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