Li-ion battery electrolytes are complex solutions composed of solvents, functional additives, and lithium salts. Lithium ion mobility is a central property affecting battery performance. The chemical and physical factors impacting charge transport in complex solutions are not fully understood. Molecular dynamics (MD) simulation is a powerful technique that can provide insight into the critical details impacting ion transport in complex electrolyte solutions. Although several studies have been reported in the literature examining lithium diffusion using MD, limited computational power has restricted simulation time scales to only tens of nanoseconds. Additionally, the simulation results have been hampered due to lack of accurate force-field parameters. In this work, the enhanced predictive reliability for battery electrolytes provided by GPU-accelerated MD using the highly efficient Desmond MD engine [1], along with the quantitative OPLS3 force-field [2] is demonstrated. Using GPU-Desmond/OPLS3, hundreds of simulations can be performed in a production environment with simulation duration in excess of 1 µs. We discuss the impact that simulation duration and force-field has on the accuracy of Li-ion electrolyte simulations.

[1] K.J. Bowers, E. Chow, H. Xu, R.O. Dror, M.P. Eastwood, B.A. Gregersen, J.L. Klepeis, I. Kolossvary, M.A. Moraes, F.D. Sacerdoti, J.K.Salmon, Y. Shan and D.E. Shaw, Proceedings of ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida November 11-17, 2006.

[2] E. Harder, W. Damm, J. Maple, C. Wu, M. Reboul, J.Y. Xiang, L. Wang, D. Lupyan, M.K. Dahlgren, J.L. Knight, J.W. Kaus, D.S. Cerutti, G. Krilov, W.L. Jorgensen, R. Abel, R.A. Friesner, J. Chem. Theory Comput., 2016, 12 (1), pp 281; DOI: 10.1021/acs.jctc.5b00864