(687d) MoSDeF-GOMC: Python software for the creation of scientific workflows for the Monte Carlo simulation engine GOMC

Monte Carlo and molecular dynamics simulations rely on several input files that include information such as atomic coordinates, molecular topology, force field parameters, and a control file that directs the behavior of the simulation engine. Traditionally, these files were generated by hand by users with expert knowledge. The need for expert knowledge makes the learning curve steep for new users, while the bespoke generation of input files is error-prone with poor reproducibility.

In this work, we present updates to MoSDeF-GOMC, a python interface to the Molecular Simulation Design Framework (MoSDeF) [1-5] that enables users to create all the input files required to perform simulations with the GPU Optimized Monte Carlo (GOMC) simulation engine [6-7]. MoSDeF-GOMC dramatically simplifies the process of building systems and assigning force field parameters. Additionally, it provides some expert-system features, guiding users towards reasonable values for numerous parameters used to control Monte Carlo simulations. When combined with the Signac software [8-9], complex workflows may be created that incorporate thousands of discrete simulations, supporting the use of MoSDeF and GOMC for high-throughput screen applications. To highlight some of the capabilities of MoSDeF-GOMC, a number of illustrative applications are presented, including the prediction of the vapor-liquid coexistence curve for the jet fuel surrogate S-8, the hydration free energy for noble gases in water, the adsorption of ethane in a metal organic framework, and gas adsorption in a polymer matrix.

References

[1] Y. Nejahi, M. Barhaghi, J. Mick, B. Jackman, K. Rushaidat, Y. Li, L. Schwiebert and J. Potoff, "GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids”, SoftwareX, vol. 9, p. 20–27, 2019.

[2] Y. Nejahi, M. Barhaghi, G. Schwing, L. Schwiebert and J. Potoff, "Update 2.70 to GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids”, SoftwareX, vol. 13, p. 100627, 2021.

[3] A. Summers, J. Gilmer, C. Iacovella, P. Cummings and C. McCabe, "MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer Films”, J. Chem. Theory. Comput., vol. 16, no. 3, p. 1779-1793, 2020.

[4] M. Thompson, R. Matsumoto, R. Sacci, N. Sanders and P. Cummings, "Scalable Screening of Soft Matter: A Case Study of Mixtures of Ionic Liquids and Organic Solvents”, J. Phys. Chem. B, vol. 123, no. 6, p. 1340–1347, 2019.

[5] MoSDeF - the Molecular Simulation Design Framework, "https://github.com/mosdef-hub”, 2019. [Online]. [Accessed March 2021].

[6] C. Klein, A. Summers, M. Thompson, J. Gilmer, C. McCabe, P. Cummings, J. Sallai and C. Iacovella, "Formalizing atom-typing and the dissemination of force fields with foyer”, Computational Materials Science, vol. 167, p. 215-227, 2019.

[7] M. Thompson, J. Gilmer, R. Matsumoto, C. Quach, P. Shamaprasad and A. Yang, "Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE) ”, Mol. Phys., vol. 118, p. e1742938, 2020.

[8] V. Ramasubramani, C. Adorf, P. Dodd, B. Dice and S. Glotzer, "signac: A Python framework for data and workflow managemen”, in Proceedings of the 17th Python in Science Conference, 152-159, 2018.

[9] C. Adorf, P. Dodd, V. Ramasubramani and S. Glotzer, "Simple data and workflow management with the signac framework”, Computational Materials Science , vol. 146, no. C, p. 220-229, 2018.