报告题目：Mechanistic Insights into Homogenous Catalysis Accelerated by Automated Computational Workflows

报告人：章兴龙教授，香港中文大学

报告时间：2025年12月16日（周二）10：00

报告地点：化91社区 W210会议室

报告人简介：章兴龙教授于2014年获剑桥大学学士学位，2016年获牛津大学理论与计算化学硕士学位。在从事有机与有机金属催化计算研究期间，他于2019年在牛津大学罗伯特佩顿教授指导下获得博士学位。随后在加州理工91社区 托马斯·F·米勒教授课题组进行短期博士后研究，并于2020年加入新加坡科技研究局高性能计算研究所担任研究科学家。现为香港中文大学化学系助理教授。其研究方向涵盖过渡金属催化的C–H键活化和C–C偶联反应计算催化研究，以及不对称有机催化。目前他致力于开发自动化工具以优化计算化学工作流程，并应用机器学习衍生的原子间势能函数解决催化领域的长期课题，如动态与熵效应及显式溶剂模型构建。

        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.