Kunran Yang, Ph.D. is the lead computational chemist in the computational and theoretical chemistry group at the Materials Discovery Research Institute, where he develops high-throughput computational workflows for accelerated materials discovery in renewable energy and eco-remediation applications. He leads molecular-level simulations and builds computational infrastructure on the lab’s high performance computing facilities and cloud clusters to reduce material innovation costs through accurate, cost-effective predictions.

Yang brings expertise in computational materials science with a focus on catalysis, energy storage, and environmental applications. During his postdoctoral research at NC State University, he developed high-throughput screening methods for materials screening in chemical looping processes and published extensively in high-impact journals. His research combines density functional theory, molecular dynamics, and machine learning approaches to predict material properties and guide experimental synthesis efforts. He joined the Materials Discovery Research Institute in 2024.

Yang holds a Ph.D. in physical chemistry from the University of Chinese Academy of Sciences.