学术讲座
报告题目：Mechanistic Insights into Homogenous Catalysis from Computational Perspective – case studies from C-H functionalisation and asymmetric catalysis
报告时间：2024-10-28 10:00
报告人： 章兴龙 助理教授
香港中文大学
报告地点：卢嘉锡202报告厅(已预约）
转播地点：翔安校区能源材料大楼3号楼会议室5，漳州校区生化主楼307教室
报告摘要：
Homogeneous catalysis plays a pivotal role in modern chemical synthesis, enabling efficient and selective transformations of various substrates. Computational tools play increasingly important roles in identifying key transition states and intermediates and unravelling complex reaction mechanisms governing these catalytic processes. Beginning with key concepts in catalysis, we will explore how chemical reactivity and selectivity can be studied computationally. Theoretical approaches, including density functional theory (DFT) and electronic structure methods, will be applied in understanding reaction mechanisms. Case studies will be presented to illustrate how theoretical insights play instrumental roles in understanding the catalysts and reaction conditions. Specifically, we will examine palladium catalysed C–H functionalisation and organocatalytic asymmetric catalysis and appreciate how computational tools can complement experimental approaches in achieving a comprehensive mechanistic understanding of complex catalytic reactions.
报告人简介：
Dr Xinglong Zhang obtained his BA degree from the University of Cambridge in 2014 and a 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. He is currently 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. Group website: https://xinglong-zhang.github.io/.
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