At the Materials Discovery Research Institute, Yidi Shen, Ph.D. is engaged in electrocatalyst design for water splitting, with an emphasis on developing a computational high-throughput workflow that integrates density functional theory, molecular dynamics, and machine learning techniques. This framework enables efficient screening of alloy catalysts to balance activity, stability, and cost, while systematically generating a database of computed properties. The curated data will be leveraged to identify promising candidates and accelerate joint validation with experimental collaborators.

Shen was a postdoctoral scholar at the California Institute of Technology, where she worked on multiscale modeling of catalysts (e.g., high-entropy alloys for ammonia synthesis, Pt-based oxides for CO₂ reduction), semiconductor surfaces chemistry, and glass formability of metallic glasses. While earning her Ph.D., her research centered on atomistic simulations and machine-learning-based force fields for complex materials design. Her work has been published in leading journals like Nature Materials, Journal of the American Chemical Society, and PNAS. She joined the Materials Discovery team in 2025.

Shen holds a M.S. in materials science and engineering from the University of Nevada, Reno and a Ph.D. in materials science and engineering from Iowa State University.