(39g) GOMC-MoSDeF: A Module for Generating the Required Files for Conducting Monte Carlo Simulations via the GOMC and MoSDeF Software

One of the most critical, time-consuming, and significant bottlenecks in molecular simulations can be generating the initial molecular configurations and the simulation engine's control files. Traditionally, constructing these GPU Optimized Monte Carlo (GOMC) files requires in-depth knowledge of molecular simulations [1, 2], which presents a significant learning curve for new users. The newly added GOMC module expands the capabilities of the existing Molecular Simulation Design Framework (MoSDeF) software [1, 2, 3, 4, 5, 6], allowing molecular configuration, connectivity, force field, and GOMC control files to be created easily via straightforward Python code. The new GOMC module’s functionality drastically reduces the entry barrier for new users [1, 2], minimizes the upfront training, and human error in the file generation.

Molecular simulations can help identify and optimize new materials or chemical systems via large-scale screening efforts, in advance of experimental testing, provided that minimal user interaction is needed to generate the required simulation files [1, 2]. In many cases, these large-scale screenings are not possible or prohibitively expensive with current experimental methods. Conventionally constructing these GOMC simulations was time and labor-intensive [1, 2], and required users to write their own detailed workflows. The recent extension of MoSDeF to support GOMC enables easy integration with the Signac software [1, 2, 3, 4, 5, 6, 7, 8, 9], allowing for a significant chemical space to be explored with minimal user interaction. A few examples using this new GOMC-MoSDeF module will highlight the new capabilities, flexibility, and end simulation results using the GOMC simulation engine [1, 2, 3, 4, 5, 6].

References

[1] 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, pp. 1779-1793, 2020.

[2] 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.

[3] 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.

[4] 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.

[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, pp. 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 management," 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, pp. 220-229, 2018.