Enhancing Machine-Learned Interatomic Potentials in Materials Science: Progressing from Accuracy to Robustness.
Speaker
Yangshuai Wang
University of British Columbia
Time
Venue
Online
Meeting Info
腾讯会议
ID: 296849485
Password：670112
Abstract
The success of molecular simulation relies heavily on the accuracy and efficiency of force-fields. A novel technique named machine-learned interatomic potentials (MLIPs) has been developing rapidly. The idea is to bridge the significant gap in accuracy and capability between ab initio electronic structure models and classical mechanistic models. The MLIPs are becoming part of the standard toolbox of computational materials science as demonstrated by the increasing number of successful applications leading to new scientific discoveries, including amorphous materials, high-pressure systems, phase diagrams and reaction dynamics of molecules. In this talk, I will provide an overview of the current state-of-the-art MLIPs, highlighting recent applications in materials science. Specifically, I will explore the transition from small-but-accurate models like the Atomic Cluster Expansion (ACE) method to large-and-robust models established using the MACE (Message passing neural networks with ACE) architecture. Furthermore, I will briefly overview alternative recent approaches in AI for Materials and address prevailing challenges and potential pathways for their resolution, laying the groundwork for the advancements in AI for Science.
Bio
Dr. Yangshuai Wang is currently a postdoctoral fellow in the Department of Mathematics at the University of British Columbia, working under the supervision of Prof. Christoph Ortner since December 2021. He obtained his PhD in computational mathematics from Shanghai Jiao Tong University in 2021. Dr. Wang's research interests lie in mathematical modeling, analysis, and their applications in materials and biomedical sciences. His work primarily focuses on advancing multi-scale methods and machine-learned interatomic potentials (MLIPs) to better understand material behaviors and biological processes.
Sponsor
Institute of Natural Sciences, Shanghai Jiao Tong University
Shanghai National Center for Applied Mathematics (SJTU Center)
