Atomistic Modeling and AI-enabled Energy Storage Materials Discovery

A priori atomistic modeling provides accurate information to enable design and discovery of materials for energy and chemicals. In energy storage, <i>beyond lithium-ion (BLI) research</i> has the potential to revolutionize consumer electronics including portable and stationary power, transportation sector, and grid energy storage. Multi-valent energy storage or economically viable Na⁺ batteries, high-density metal-air, metal-sulfur batteries, or grid-storage systems are considered in the beyond lithium-ion research and development. <i>All these research efforts require significant a priori computations for materials discovery, property prediction, and optimization using atom-atom and molecule by molecule approaches</i>. Atomistic modeling can provide <i>a priori</i> data to accelerate discovery of electrolytes, electrodes, and membranes to reduce the cost and time of discovery. Coupled with data science and multi-scale techniques, atomistic modeling can address prediction of molecular level properties of materials (redox potentials, solvation, spectroscopic, and reactivity) to down-select <i>optimal materials or material combinations</i>. In this presentation, I will describe some of our recent efforts in active learning coupled with large scale first principles simulations to down select/optimize desired molecules for flow battery technology. I will also describe some of our quantum chemistry-informed molecular property predictions of thousands of molecules and data driven approach to study longer time scale diffusion of ions for multivalent battery concepts.