We are an interdisciplinary computational research group in the Department of Materials Design and Innovation at the University at Buffalo. We build physics-informed, data-driven machine-learning methods to capture the fundamental laws of materials thermodynamics and surface kinetics from atomistic simulations and characterization data and empower all group members to construct materials-centric AI solutions for chemical transformation and energy technologies in order to combat the most urgent societal challenges, such as energy poverty, food insecurity, critical minerals, and pollution.

Our group is part of a highly interdisciplinary academic department with one-of-a-kind graduate programs that are uniquely positioned at the intersection of materials science and data science. We aim to train graduate students with transdisciplinary skillsets across materials modeling, electrochemistry, catalysis, surface science, data analytics, and machine learning to meet the career demands of an increasingly data-driven and cross-disciplinary world. While we are primarily a computational research group, we are deeply interested in having our students work with experimental collaborators to integrate high-throughput simulations and physics-driven machine learning to facilitate efficient, accurate, and rigorous elucidation of new atomistic insights and physical principles from convoluted experimental characterization data.