報告題目：Mechanistic Insights into Homogenous Catalysis Accelerated by Automated Computational Workflows

報告人：章興龍教授，香港中文大學

報告時間：2025年12月16日（周二）10：00

報告地點：化學院W210會議室

報告人簡介：章興龍教授于2014年獲劍橋大學學士學位，2016年獲牛津大學理論與計算化學碩士學位。在從事有機與有機金屬催化計算研究期間，他于2019年在牛津大學羅伯特佩頓教授指導下獲得博士學位。隨后在加州理工學院托馬斯·F·米勒教授課題組進行短期博士后研究，并于2020年加入新加坡科技研究局高性能計算研究所擔任研究科學家。現(xiàn)為香港中文大學化學系助理教授。其研究方向涵蓋過渡金屬催化的C–H鍵活化和C–C偶聯(lián)反應計算催化研究，以及不對稱有機催化。目前他致力于開發(fā)自動化工具以優(yōu)化計算化學工作流程，并應用機器學習衍生的原子間勢能函數(shù)解決催化領(lǐng)域的長期課題，如動態(tài)與熵效應及顯式溶劑模型構(gòu)建。

        報告摘要：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.