In the Wolverton Research Group, our research focuses on using computational tools and artificial intelligence to tackle today’s most pressing challenges in energy and materials science. We combine molecular dynamics simulations, first-principles calculations, data-driven modeling, and machine learning techniques to understand, predict, and design new materials with targeted properties. Our work spans a wide range of topics, including batteries, catalysts, structural materials, and functional materials, with the goal of accelerating materials discovery and improving performance, stability, and sustainability. In addition, we develop and maintain the Open Quantum Materials Database (OQMD), a large-scale materials database that enables big-data-driven materials research and provides the broader materials community with powerful resources for large-scale screening and discovery. By bridging fundamental theory with high-throughput computation and modern AI approaches, we aim to build predictive frameworks that can guide experiments and enable faster, more efficient development of next-generation energy materials.

Researchers who share our vision and interests are encouraged to contact Prof. Chris Wolverton.

🎖️Group Highlights!