LEADERSHIP
DIRECTOR
Dane Morgan
Harvey D. Spangler Professor
Department of Materials Science & Engineering
UW-Madison
Dane Morgan is a computational materials scientist who integrates atomistic modeling, thermokinetics, and data science to understand and predict materials properties. He is using AI and machine learning for predicting materials properties and accelerating simulation and characterization.
Jiamian Hu
Associate Professor
Department of Materials Science & Engineering
UW-Madison
Prof. Hu’s research group works on microstructure-aware integrated materials and device modeling and design, using both phase-field simulations and AI. He brings expertise in microstructure-aware computational materials design, for which he develops and applies phase-field models and AI methods to understand and predict the process-microstructure-property (PMP) relationship and microstructure evolution, as well as accelerate the inverse design of processing conditions and microstructures in ceramic, alloy, and composite materials.
Hyunseok Oh
Assistant Professor
Department of Materials Science & Engineering
UW-Madison
Prof. Oh is an experimental metallurgist whose group studies process-structure-property-performance relationships to design advanced metallic materials. He explores the use of Large Language Models to enhance systems-engineering approaches in alloy design.
Joel Paulson
Gerald and Louise Battist Associate Professor
Department of Chemical and Biological Engineering
UW-Madison
Prof. Paulson’s research group develops advanced Bayesian optimization and sequential experimental design methods to accelerate materials/molecular design and discovery, guiding both costly experiments and high-fidelity simulations under uncertainty. His work aims to build new tools for learning data-efficient, interpretable models that not only improve decision-making in closed-loop workflows but also have the potential to yield transferable scientific insight.
Xuhui Huang
Hirschfelder Chair in Theoretical Chemistry & Theoretical Chemistry Institute Director
Department of Chemistry
UW-Madison
The main goal of the Huang Lab is to develop new chemical theories to propel the advancements of AI for science, particularly in studying biomolecular dynamics. Research areas include elucidation of functional conformational changes in gene transcription, elucidation of molecular recognition and self-assembly, development of machine learning and Generalized Master Equation model for biomolecular dynamics, development of Integral Equation theories for solvation, and development of deep learning methods to predict protein-ligand and protein-RNA interactions.