Oct. 19, 2023 – Shyue Ping Ong: Universal Graph Deep Learning Models for Unconstrained Materials Design

In silico materials design often involves the exploration of vast, diverse chemical spaces. While ab initio methods have been phenomenally successful in materials simulations, their scope of application has always been constrained by their high cost and poor scaling. In this talk, I will highlight the recent development of graph deep learning models that can universally work across the periodic table, revolutionizing our ability to explore the entire universe of materials at unprecedented scales and accuracy. Such models can be considered as “foundational AI models” for materials chemistry, with broad applications in the dynamic simulations and discovery of materials. Finally, I will also provide some perspectives on the remaining challenges and opportunities for such models.

Shyue Ping Ong is a Professor of NanoEngineering at the University of California, San Diego. He obtained his PhD from the Massachusetts Institute of Technology in 2011. He leads the Materials Virtual Lab at UCSD, a dynamic group of materials scientists focusing on the interdisciplinary application of materials science, computer science, and data science to accelerate materials design. He is one of the founding developers of the Materials Project, a DOE-funded initiative to make the computed properties of all known materials publicly available for materials innovation.  He is also the principal developer of Python Materials Genomics (pymatgen), an open-source materials analysis library that is used by hundreds of thousands of users worldwide. Dr Ong is a recipient of the US Department of Energy Early Career Research Program and the Office of Naval Research Young Investigator Program awards and a Clarivate Highly Cited Researcher in 2021 and 2022.