报告人：章兴龙 香港中文大学

报告时间：2025年12月9日15:00

报告地点：嘉锡楼413

组织单位：有机合成与功能福建省高校重点实验室

联系人：杨凯

联系电话：1805970235

报告摘要：Homogeneous catalysis forms a cornerstone of modern organic synthesis, yet factors governing chemical reactivity and selectivity are often challenging to discern from experiments alone. In this seminar, I will show how state-of-the-art computational chemistry can yield detailed mechanistic insights into various catalytic systems. Through case studies on transition-metal catalysis and asymmetric ion-pairing / cascade processes, I will discuss how density functional theory and related methods reveal the operative pathways, the origin of chemo-, regio- and enantioselectivity, and the roles of ligand environment, non-covalent interactions and reaction microenvironment. These examples highlight how mechanistic understanding may suggest new substrate classes, leaving groups and ligands, and rationalize unexpected experimental trends. I will then introduce chemsmart, an open-source Python toolkit we develop to automate quantum chemical workflows from input generation to job submission and results analysis. By integrating mechanistic insight with reproducible, scalable workflows, we aim to equip researchers with an extensible framework for data-rich, mechanistically guided catalyst and reaction design.

报告人简介：Dr Xinglong Zhang obtained his BA degree from the University of Cambridge in 2014 and Master of Science in Theoretical and Computational Chemistry from the University of Oxford in 2016. Working on computational studies of organic and organometallic catalysis, he obtained his Doctor of Philosophy under Prof Robert Paton at the University of Oxford in 2019. After a brief postdoctoral stint under Prof Thomas F. Miller at Caltech, he joined the Institute of High Performance Computing (IHPC), A*STAR as a research scientist in 2020. Dr Zhang is currently an Assistant Professor at the Chemistry Department of the Chinese University of Hong Kong (CUHK). His research interests include computational catalysis in transition metal-catalyzed C–H functionalization and C–C coupling reactions, and asymmetric organocatalysis. His group is active in developing automation tools to streamline computational chemistry practice and applying machine learning derived interatomic potentials to address longstanding topics in catalysis such as dynamical and entropic effects and explicit solvent modelling. More information can be found on group website: //xinglong-zhang.github.io/.