Data-driven Multiscale Computational Studies of Materials
Abstract:
In the first part, I will introduce my studies on the mechanics of silicon (Si) electrodes in lithium (Li)-ion batteries. Si has a specific capacity of 10 times higher than conventional graphite anode, making it a promising anode material for high-capacity Li-ion batteries. However, the large capacity of Li ions causes a ~300% volume expansion in Si, which further leads to cracking, delamination, and capacity drop. To understand the coupling relations between mechanics and electrochemistry, I conducted a multiscale computational study spanning density functional theory, molecular dynamics simulations, and finite element method. In the second part, I will present my studies on the data-driven stochastic modeling of materials. Using the method of stochastic reduced-order modeling, I modeled the variabilities of the mechanical behavior of graphene in computations, resembling what we observed in multiple experimental measurements. This work enables us to close the gap between experiments and simulations.
Bio:
Dr. Haoran Wang has been an assistant professor in the Department of Mechanical & Aerospace Engineering at Utah State University since August 2020. Before that, he was a postdoctoral research associate in the Department of Civil & Environmental Engineering at Duke University. He obtained his Ph.D. in Aerospace Engineering at the University of Illinois Urbana-Champaign in May 2018. His research is focused on solid mechanics, multiscale computations, and data-driven modeling.