AI-Enabled Materials Structure-Property-Synthesizability Predictions

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Name Yousung Jung Affiliation Department of Chemical and Biological Engineering, Seoul National University

Abstract

Discovery of new molecules and materials with desired properties is a practical goal of chemical research. A promising way to significantly accelerate the latter process is to incorporate all available knowledge and data to plan the synthesis of the next materials. In this talk, I will present several directions to use informatics and machine learning to efficiently explore chemical space. I will first describe methods of machine learning for fast and reliable predictions of materials and molecular properties. With these tools in place for property evaluation, I will then present a few generative frameworks that we have recently developed to allow the inverse design of molecules and materials with optimal target properties, either in the compositional space or structural space. One general challenge in digital discovery is that many of the molecules and materials that are computationally designed are often discarded in the laboratories since they are not synthesizable. I will thus lastly spend some time to talk about the synthesizability of molecules and materials, either by predicting the synthesis pathways (retrosynthesis) or chemical reactivity. Several challenges and opportunities that lie ahead for further developments of accelerated chemical platform will be discussed.

Biography

"AI-Enabled Materials Structure-Property-Synthesizability Predictions"

Yousung Jung is a Professor of Chemical and Biological Engineering at Seoul National University. His research background and interests involve quantum chemistry and machine learning to develop efficient methods for fast and accurate simulations of complex molecular and materials systems, and their applications towards the understanding of molecules and materials for new discovery. Some of his recent works include the use of data science and machine learning to understand the structure-property-synthesizability relations for molecules and materials and use the obtained knowledge for inverse design. He received his Ph.D. in Theoretical Chemistry from University of California, Berkeley, with Martin Head-Gordon. After a postdoctoral work at Caltech with Rudy Marcus, he joined the faculty at KAIST in 2009, and moved to Seoul National University in 2023. He received the Hanseong Science Award by Hanseong Son Jae Han Foundation, KAIST Technology Innovation Award, Pole Medal by Asia-Pacific Association of Theoretical and Computational Chemists, Korean Chemical Society Young Physical Chemist Award, and KCS-Wiley Young Chemist Award.