Kevlishvili group
Kevlishvili group develops new ways to represent molecules and materials as data, so that computers can see chemistry the way chemists do. Using these representations, we build AI and simulation tools to design catalysts, smart polymers, and other functional systems for clean energy, sustainability, and medicine. By reinventing how chemical information is encoded and explored, we aim to make discovery faster, more predictive, and more accessible across the scientific community.
Highlights
Our Research
We develop canonical string representations for inorganic and organometallic complexes, foundation models that learn the logic of catalytic cycles, and transfer learning pipelines for mechanophore discovery. Our work spans catalysis, photochemistry, electrochemistry, mechanochemistry, and pharmaceutical design.
Our Projects
From T-REX — a canonical grammar for transition-metal complexes — to foundation models for catalysis and multi-stage mechanophore design pipelines, we build computational tools that make inorganic chemical space navigable and predictive.
Our Team
We are a new and growing group in the Department of Chemistry and Biochemistry at Baylor University. Our team brings together expertise in computational chemistry, machine learning, and data science. We are actively recruiting motivated students and postdocs.