About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Advances in Ceramic Materials and Processing
|
Presentation Title |
Computational Study of Ionic Transport in Lithium Garnet Oxides with Machine-learning Interatomic Potentials |
Author(s) |
Wei Lai |
On-Site Speaker (Planned) |
Wei Lai |
Abstract Scope |
Ionic transport is of fundamental importance in solid electrolytes. 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. In this talk, I will show some of our recent studies of applying MLIPs to study the ionic transport in lithium garnet oxides. |
Proceedings Inclusion? |
Planned: |