Quantum Chemistry-Enabled Machine Learning for Designing Improved Molecular Materials for Energy and Conversion.

<i>A priori</i> and reliable simulations can enable timely and cost-efficient design and discovery of materials for energy. Therefore, ‘<i>Let’s Start from Computing</i>’ is an optimal approach to initialize modern day R&D processes. In energy storage, <i>beyond lithium-ion (BLI) research</i> has the potential to revolutionize consumer electronics including portable and stationary power, transportation, and grid energy storage sectors. Multi-valent (Mg, Ca, Zn) energy storage or economically viable monovalent (Na, K) 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 R&D efforts require significant fundamental knowledge via a priori computations for materials discovery, property prediction, and optimization</i>. Atomistic modeling when coupled with reliable Artificial Intelligence (AI) approaches can provide <i>accurate </i> insights to <i>accelerate </i>discovery of <i>optimal</i>electrolytes, electrodes, and membranes for BLI systems to <i>reduce the cost</i>. Thus, coupled with AI and multi-scale simulations 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 (2018-2022) in active learning coupled with large scale first principles simulations to down select/optimize desired molecules for <i>flow battery</i> technology. This concept can be utilized for design of experiments using autonomous experimentation. Additionally, I will describe some of our quantum chemistry-informed molecular property predictions redoxmers and liquid organic hydrogen carriers. In addition to molecules, I will present. A data driven approach to study longer time scale diffusion of ions for multivalent battery concepts. Finally, I will describe our computational catalysis program development timeline with details of a recent data-driven approach for catalytic property prediction using high performance periodic density functional computing and deep learning .