Affiliations

• Materials Science and Engineering
• Applied Physics

Education Profile

• Post-Doctoral Fellow, University of Toronto, 2022-2024
• Ph.D., Université Paris-Saclay, 2018-2021
• M.Eng., Sun Yat-Sen University, 2016-2018
• B.Eng., Sun Yat-Sen University, 2012-2016

Research Interests

​Professor Li specializes in integrating artificial intelligence with computational modeling to enable and accelerate autonomous research in the physical sciences. His work emphasizes the creation of robust and efficient machine learning methodologies for both computational and experimental design. Additionally, he focuses on developing automated workflows and deep learning models to accelerate atomistic simulations and enhance materials informatics. His research of interests encompasses a variety of materials, including structural materials, catalysts, batteries, MOFs, and solar cells.​

Selected Publications

• K Li et al., Probing out-of-distribution generalization in machine learning for materials, Communications Materials. 2025
• K Li et al., Efficient first principles based modeling via machine learning: from simple representations to high entropy materials, Journal of Materials Chemistry A (Front Cover). 2024
• K Li et al., Exploiting redundancy in large materials datasets for efficient machine learning with less data, Nature Communications. 2023.
• K Li et al., A critical examination of robustness and generalizability of machine learning prediction of materials properties”, npj Computational Materials. 2023
• K Li et al., A Call for Caution in the Era of AI-Accelerated Materials Science, Matter. 2023