AI molecular catalysis: Data, Intelligence and Modelling

 

AI molecular catalysis: Data, Intelligence and Modelling

 

Date: 9^th June, 2026 (Tuesday)

Time: 5 - 6 pm (HKT)

 

Venue: Lecture Theatre P2, Chong Yuet Ming Physics Building

 

 

Center of Basic Molecular Sciences

Tsinghua University

 

Sanzhong Luo, Professer of Chemistry, Tsinghua University; Director Center of Basic Molecular Sciences and Beijing Key Laboratory of Intelligent Synthesis,. He received his B.S. (1999), M.S. (2002) and Ph.D. (2005) from Zhengzhou University, Nankai University and the Institute of Chemistry, Chinese Academy of Sciences (ICCAS), respectively. He was a visiting scholar in the Ohio State University (2004-2005) and Stanford University (2009). He worked in ICCAS during 2005-2018 and has been professor at Tsinghua University since 2018. His laboratory focuses on asymmetric catalysis, bio-inspired catalysis and AI chemistry.

 

Catalysis lies at the core of chemical synthesis, enabling ~80% of chemical production, with three Nobel Prizes awarded this century for breakthroughs in molecular catalysis. Yet despite revolutionary advances in experimental and computational capabilities, the research paradigm of molecular catalysis has remained largely unchanged for a century. In recent years, the rapid rise of big data and artificial intelligence (AI) technologies has opened a new chapter in materials science research, offering fresh opportunities for the transformative evolution and development of molecular catalysis. Drawing lessons from history, physical organic chemistry research—built on quantitative data—has established quantitative structure-activity relationship (QSAR) models using physical and mathematical approaches, providing insights and guidance for AI-driven molecular catalysis. This presentation will focus on AI-powered molecular catalysis research guided by the principles and strategies of physical organic chemistry. Key topics will include the development of the universal molecular descriptor SPOC, the construction of standardized databases on molecular catalysis, and the application of AI technologies in the discovery and optimization of novel reactions.

 

[1] Tan, Z.; Yang, Q.; Luo, S. AI molecular catalysis: where are we now? Org. Chem. Front., 2025, 12, 2759-2776.

 

[2] (a) Li, J.; Li, M.; Yang, Q.; Luo, S. Nature Comm., 2026, 17, 3356. (b) Li, J.; Xiao, X.; Yang, Q.; Zhao, B.; Luo, S. Angew. Chem. Int. Ed. 2026, e2455429. (c) Tan, Z.; Yang, Q.; Zhang, L.; Luo, S. Angew. Chem. Int. Ed. 2026, e23874. (d) Cheng, L.; Tan, Z.; Jia, Z.; Lin, Q.; Yang, Q.; Luo, S. Nature Synth., 2026, 5, 455-465. (e) Yang, Q.; Liu, Y. D.; Zhang, W.; Zhang, L.; Chen, Y.; Luo, S. CCS Chem. 2026, in revision.

 

[3] Huang, M.; Zhang, L.; Pan, T.; Luo, S. Z. Science 2022, 375, 869-874. (b) Jia, Z.; Zhang, L; Luo, S. J. Am. Chem. Soc. 2022, 144, 10705–10710. (c) Lin, Q.; Duan, Y.; Li, Y.; Jian, R.; Yang, K.; Jia, Z.; Xia, Y. Zhang, L.; Luo, S. Nat. Commun. 2024, 15, 6900; (d) Jia, Z.; Cheng, L.; Zhang, L.; Luo, S. Nat. Commun. 2024, 15, 4044. (e) Zhang, S.; Cheng, L.; Qi, J.; Jia, Z.; Zhang, L.; Jiao, L.; Guo, X.; Luo, S. Z. CCS Chem. 2024, 6, 2420-2426.