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SUMMARY:ChE Seminar: Stephen Lam\, University of Massachusetts\, Lowell\,
 “AI-Guided Chemical Science for Advanced Nuclear Energy Systems: Chemist
 ry\, Structure\, and Properties Across the Periodic Table” 
DTSTART;VALUE=DATE-TIME:20260210T163000Z
DTEND;VALUE=DATE-TIME:20260210T173000Z
UID:511269640048
DESCRIPTION:Host: Dimitrios Maroudas and Zhu ChenSponsored by the Departme
 nt of Chemical and Biomolecular Engineering  Stephen Lam University of M
 assachusetts Lowell “AI-Guided ChemicalScience for Advanced Nuclear Ene
 rgy Systems: Chemistry\, Structure\, andProperties Across the Periodic Tab
 le” Tuesday\, February 10\, 2026\, 11:30 a.m.201 LGRT\, UMass Amherst(R
 efreshments at 11:15 a.m.) Abstract:A central challenge to deployingadvan
 ced nuclear technologies lies in our ability to accurately characterize\,p
 redict\, and monitor the chemistry of materials throughout the operational
  lifeof reactor and fuel cycle. In fission and fusion environments\, nucle
 artransmutation results in a vast array chemical products that are formed 
 underextreme conditions including high temperatures\, pressures\, and radi
 ationfields. Here\, current experimental and computational approaches are 
 eitherinsufficiently accurate or expeditious for assessing these design sp
 aces. Assuch\, it is unlikely that we will achieve the robust chemical und
 erstandingrequired for commercial deployment of advanced nuclear energy sy
 stems usingconventional research paradigms. This talk will discuss our lat
 est advances inapplying artificial intelligence (AI) to overcome these cha
 llenges for studyingthe chemistry-structure-property relationships in molt
 en salt\, which include 1)machine learning (ML)-assisted atomistic simulat
 ion for speed and accuracy\, 2)chemistry-informed ML for learning the ther
 mal properties of molten saltsacross the periodic table and generative AI 
 for targeted-property design\, and3) machine learning-enhanced characteriz
 ation and online monitoring withspectroscopic methods. We will show how st
 ate-of-the-art methods have beenapplied for uncovering structure-property 
 of molten salts with unprecedentedspeed and resolution and discuss future 
 opportunities for improvement in eachof these areas. Bio:Stephen Lam is t
 he Director of Nuclear Engineering\, andAssistant Professor of Chemical En
 gineering at the University of MassachusettsLowell. His research focuses o
 n integrating artificial intelligence andmaterials simulation with experim
 ental characterization techniques for thepurpose of understanding chemical
  structure\, reactions and propertyrelationships in advanced energy materi
 als. Stephen obtained a PhD in NuclearScience and Engineering in 2020 from
  the MIT\, and BS in Chemical Engineering in2013 from the University of Br
 itish Columbia. He was the recipient of the U.S.Department of Energy Early
  Career Award\, and U.S. Nuclear RegulatoryCommission’s Distinguished Fa
 culty Advancement Award in 2024. His work has beenpublished in over 30 pee
 r-reviewed articles (including JACS Au\, Nature MachineIntelligence\, npj 
 Computational Materials\, Chemical Science) in areas ofmachine learning\, 
 molten salt chemistry\, tritium interactions with materials\,carbon materi
 als\, and high-temperature ceramics.
LOCATION:LGRT 201
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