My research lab will develop theoretical frameworks, accelerate molecular simulations, and work closely with experimentalists to unravel the interplay between chemical reactions and molecular topology in large-scale soft-condensed matter systems. My background in statistical mechanics will be pivotal for understanding the thermodynamic and kinetic aspects of reactions and transport in these materials. Expertise in ML-forcefields (MLFFs) will enable the simulation of extensive systems over long timescales, while deep generative models and probabilistic AI modeling will facilitate the efficient exploration of vast chemical spaces. Concepts from topology and thermodynamics will aid in elucidating structure–property relationships, and quantum mechanical (QM) calculations will provide high-accuracy insights into the electronic structure and reactivity of these systems. These computational efforts will be coupled with close experimental collaboration, allowing us to rationally design materials and processes. The lab will initially focus on: (1) ionic liquids, (2) functional charged peptides, and (3) metal–organic framework design for enzyme immobilization.
Previous Research:
I earned my Ph.D. in Computational Modeling and Simulation at the University of Pittsburgh with Prof. J. Karl Johnson, where we developed ML-driven methods—primarily ML-forcefields—that not only accelerate molecular simulations but also reveal fundamental chemical phenomena. Our work on functionalized graphane membranes uncovered their superior anhydrous proton conductivity and elucidated the underlying proton transfer mechanism. We also introduced Reactive Active Learning (RAL), which accurately models reactions across gas-phase, condensed-phase, and heterogeneous catalytic systems. In parallel, DeepCDP enabled us to predict electron densities, compute accurate dipole moments, and gain insights into processes such as proton-coupled electron transfer (PCET). This integrated approach bridges advanced simulation techniques with pressing scientific questions in material design and reaction mechanisms.
At the University of Chicago, my current postdoctoral research with Professors Andrew Ferguson and Junhong Chen focuses on designing sensitive and selective sensors for rapid PFAS detection using small biomolecules. I develop an AI-driven Bayesian optimization active learning approach that combines molecular docking, enhanced sampling, and experimental validation. This multi-fidelity high-throughput screening method has led to the discovery of new chemical probes that outperform those used in earlier sensors. We also employ models to interpret the roles of non-bonded interactions and entropy in PFAS binding, establishing design rules for the broader application of molecular docking. Additionally, we are developing methods to accelerate the discovery of essential chemical manifolds in dynamical systems.
Teaching Interests:
My teaching philosophy centers on active, inquiry-based learning where theory and practice intersect. Drawing on my experience mentoring both graduate and undergraduate students in computational chemistry research, I strive to create an inclusive classroom that fosters curiosity and critical thinking. I look forward to teaching courses such as statistical mechanics, molecular simulations, computational machine learning in chemical engineering, chemical reaction engineering, and thermodynamics—each designed to equip students with the analytical and practical skills needed to innovate in today’s chemical engineering landscape.