CBE Seminar: Stephen Lam, UMass Lowell, “AI-Guided Chemical Science for Advanced Nuclear Energy Systems: Chemistry, Structure, and Properties Across the Periodic Table”
Content
Hosts: Dimitri Maroudas and Zhu Chen
Abstract:
A central challenge to deploying advanced nuclear technologies lies in our ability to accurately characterize, predict, and monitor the chemistry of materials throughout the operational life of reactor and fuel cycle. In fission and fusion environments, nuclear transmutation results in a vast array chemical products that are formed under extreme conditions including high temperatures, pressures, and radiation fields. Here, current experimental and computational approaches are either insufficiently accurate or expeditious for assessing these design spaces. As such, it is unlikely that we will achieve the robust chemical understanding required for commercial deployment of advanced nuclear energy systems using conventional research paradigms. This talk will discuss our latest advances in applying artificial intelligence (AI) to overcome these challenges for studying the chemistry-structure-property relationships in molten salt, which include 1) machine learning (ML)-assisted atomistic simulation for speed and accuracy, 2) chemistry-informed ML for learning the thermal properties of molten salts across the periodic table and generative AI for targeted-property design, and 3) machine learning-enhanced characterization and online monitoring with spectroscopic methods. We will show how state-of-the-art methods have been applied for uncovering structure-property of molten salts with unprecedented speed and resolution and discuss future opportunities for improvement in each of these areas.
Bio:
Stephen Lam is the Director of Nuclear Engineering, and Assistant Professor of Chemical Engineering at the University of Massachusetts Lowell. His research focuses on integrating artificial intelligence and materials simulation with experimental characterization techniques for the purpose of understanding chemical structure, reactions and property relationships in advanced energy materials. Stephen obtained a PhD in Nuclear Science and Engineering in 2020 from the MIT, and BS in Chemical Engineering in 2013 from the University of British Columbia. He was the recipient of the U.S. Department of Energy Early Career Award, and U.S. Nuclear Regulatory Commission’s Distinguished Faculty Advancement Award in 2024. His work has been published in over 30 peer-reviewed articles (including JACS Au, Nature Machine Intelligence, npj Computational Materials, Chemical Science) in areas of machine learning, molten salt chemistry, tritium interactions with materials, carbon materials, and high-temperature ceramics.