卢嘉锡讲座
报告题目：Machine Learning Meets Quantum Chemical Predictions of Reactivity and Selectivity
报告时间：2025-10-29 10:30
报告人： Prof. Robert S. Paton
Department of Chemistry, Colorado State University, USA
报告地点：卢嘉锡楼202报告厅
转播地点：翔安校区能源材料大楼3号楼会议室1、3，漳州校区生化主楼307教室
报告摘要：
Quantum chemical models of reaction mechanism and selectivity provide a powerful tool to explain the outcome of laboratory experiments. However, since many reactions involve several steps and multiple conformers, the computational expense of QM approaches often prevent their application to predict reaction outcomes more broadly. Surrogate machine-learning models with quantum chemical accuracy at a fraction of the computational cost are set to transform the accessibility of computational predictions of reactivity and selectivity. We have developed machine learning approaches utilizing data from QM studies to generate surrogate models for the large-scale prediction of various atomic and molecular properties. Graph neural networks harnessing different two and three-dimensional molecular representations show excellent predictive accuracy, particular for atom-level and bond-level properties such as spin densities, chemical shifts, and bond dissociation thermochemistry. In this talk, I discuss the application of these models to high-throughput predictions of reactivity and selectivity of heteroaromatic functionalization, goal-directed molecular optimization of stable organic radicals, and prediction of triplet sensitization of organic substrates.
报告人简介：
Professor Robert S. Paton, Fixman-Ladanyi Endowed Chair Professor (2023-Present), Department of Chemistry, Colorado State University. His research focus on integrating computational chemistry, machine learning, and experimental approaches, organic reaction mechanisms, catalyst design, photocatalysis, radical chemistry, biocatalysis. He has published over 200 papers in top-tier journals including Nature, Science, JACS, and Angew. Chem. Int. Ed. He also serves on editorial boards of Trends in Chemistry, Tetrahedron: Chemistry, and Chemistry Methods.
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