The ECS Detroit Section invites you to Dr. Wei Lai’s presentation, “Study of Ionic Transport in Battery Materials with Machine Learning Interatomic Potentials,” in person at Mercedes-Benz Research & Development North America, Inc. at 1800h ET on March 9, 2023.
Dr. Wei Lai
Associate Professor, Chemical Engineering and Materials Science Department
Michigan State University
Date: March 9, 2023
Location: Mercedes-Benz Research & Development North America, Inc.
35555 W 12 Mile RD
Farmington Hills, MI 48331-3139
1800h ET – Dinner
1900h ET – Speaker Presentation
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Ionic transport is of fundamental importance in batteries as both battery electrolytes and electrodes conduct ions. Atomistic simulations are a group of computational methods that provide atomic-scale visualization of ionic transport and its quantification. Atomistic simulations based on the density functional theory (DFT) are the most accurate but are limited in length and time scales. Conversely, simulations based on conventional interatomic potentials (CIP), also called force fields, are the most efficient but limited in their accuracy. The recent development of machine learning interatomic potentials (MLIPs) has made it possible to have computational engines that combine the advantages of both DFT and CIP. Recent studies of applying MLIPs to study the ionic transport in lithium garnet oxides (lithium-ion solid electrolyte) and sodium nickel titanates (sodium-ion electrode) are reviewed.
Dr. Wei Lai
Dr. Wei Lai is Associate Professor in the Chemical Engineering and Materials Science department at Michigan State University (MSU). He received his BSc in Materials Science and Engineering at the University of Science and Technology of China. In 2007, Dr. Lai completed his PhD in Materials Science at the California Institute of Technology. Before joining MSU, he was a postdoctoral associate in the Materials Science and Engineering department at the Massachusetts Institute of Technology. His research interests focus on materials for energy storage and conversion through integrated experimental and computational methods.
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