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Start of funding 01.01.2025
Machine-learning accelerated simulation pipeline for Li-ion battery electrolytes
Prof. Dr. Christopher J. Stein
Technische Universität München
School of Natural Science
Prof. Dr. Bingqing Cheng
University of California, Berkeley
Department of Chemistry
Li-ion batteries with liquid electrolytes are still by far the most common energy storage devices for mobile applications ranging from smartphones to electric vehicles. Despite this tremendous commercial success, there is a substantial lack of understanding of the elementary electrochemical processes on an atomistic scale. In this project, we combine state-of-the-art machine learning potentials with sophisticated enhanced sampling methods to obtain an unprecedented holistic picture of these processes. This project will yield a unique simulation protocol for the structural exploration of Li-ion battery electrolytes based on accurate long-range neural-network potentials and efficient structural mapping and reaction path analysis. The results obtained in this study will be extendable to general liquid electrolyte-additive combinations.