We are an interdisciplinary computational research group in the Department of Materials Design and Innovation at the University at Buffalo. We aspire to combine 🤖 data science and machine learning with ⚛️ materials physics and surface chemistry to elucidate new physical principles and accelerate materials discovery for transformative opportunities in ⚡ decarbonization and 🧪 sustainability.
Our work focuses on understanding and optimizing the formidable structural, compositional, and mechanistic complexities of materials and interfaces under various essential reaction conditions relevant to negative emissions science and renewable energy technologies, with a center on electrochemistry and catalysis.
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 solutions for the most urgent societal challenges, such as climate change, pollution, energy poverty, and food insecurity.