ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
KRao_ANN_ChemRXiv_V4.pdf (1.02 MB)
0/0

Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes Using Artificial Neural Networks

preprint
submitted on 06.05.2020 and posted on 07.05.2020 by Karun Kumar Rao, Yan Yao, Lars Grabow
There is great interest in solid state lithium electrolytes to replace the flammable organic electrolyte for an all solid state battery. Previous efforts trying to understand the structure-function relationships resulting in high ionic conductivity materials have mainly relied on ab initio molecular dynamics. Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a few hundred atoms. Herein, we investigate using artificial neural networks (ANN) to accelerate the calculation of high accuracy atomic forces and energies used during molecular dynamics (MD) simulations, to eliminate the need for costly ab initio force and energy evaluation methods, such as density functional theory (DFT). After carefully training a robust ANN for four and five element systems, we obtain nearly identical lithium ion diffusivities for Li10GeP2S12 (LGPS) when benchmarking the ANN-MD results with DFT-MD. To demonstrate the power of the outlined ANN-MD approach we apply it to a doped LGPS system to calculate the effect of concentrations of chlorine on the lithium diffusivity at a resolution that would be unrealistic to model with DFT-MD. We find that ANN-MD simulations can provide the framework to study systems that require a large number of atoms more efficiently while maintaining high accuracy.

Funding

NASA Grant # 80NSSC17K0148

Seed Funding for Advanced Computing (SeFAC) grant from the Center for Advanced Computing and Data Systems (now Hewlett Packard Enterprise Data Science Institute) at the University of Houston

Texas Center for Superconductivity (TcSUH)

History

Email Address of Submitting Author

grabow@uh.edu

Institution

University of Houston

Country

United States of America

ORCID For Submitting Author

0000-0002-7766-8856

Declaration of Conflict of Interest

We declare no conflict of interest.

Version Notes

Manuscript before submission to Advanced Theory and Simulations

Exports