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Materials Science Seminar Series presents Dr. Rajeev Assary on Thursday, October 12, from 4 to 5 p.m. The seminar is hosted by Professor Dawei Feng and will be held in MS&E building room 265. Dr. Rajeev Assary will be discussing Computational Insights into Molecular Materials for Energy.
A priori and reliable simulations can enable timely and cost-efficient design and discovery of materials for energy. Therefore, ‘Let’s Start from Computing’ is an optimal approach to initialize modern day R&D processes. In energy storage, beyond lithium-ion (BLI) research 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. All these R&D efforts require significant fundamental knowledge via a priori computations for materials discovery, property prediction, and optimization. Atomistic modeling when coupled with reliable Artificial Intelligence (AI) approaches can provide accurate insights to accelerate discovery of optimal electrolytes, electrodes, and membranes for BLI systems to reduce the cost. 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 optimal materials or material combinations. In this presentation, I will describe some of our recent efforts (2019-2023) in active learning coupled with large scale first principles simulations to down select/optimize desired molecules for flow battery 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.
Rajeev Assary obtained his PhD degree in Computational Chemistry in 2005, from The University of Manchester, UK. Dr. Assary held postdoctoral positions in University of Manchester and Northwestern University prior to joining Argonne National Laboratory in 2009. At present, he is a Group Leader at Materials Science Division of Argonne National Laboratory. Dr. Assary’s research interests include fundamental and applied aspects of computational modeling based on quantum chemistry and machine learning in biomass catalysis, molecular discovery, and ‘beyond lithium ion’ energy storage systems. He has published over 120 papers in peer reviewed journals.