时间：2024-03-11

地点：新教二楼209

主持人：吉琳

主讲人：朱通  华东师范大学化学与分子工程学院/上海纽约大学物理系教授，北京科学智能研究院（AISI）成员。2022年获得国家优秀青年基金资助。2013年博士毕业于精密光谱科学与技术国家重点实验室。2016至2018年，台湾“中研院”访问学者。主要研究方向是理论和计算化学。主要发展机器学习、量子化学和分子动力学模拟算法研究复杂体系的化学反应动力学问题，包括金属离子与蛋白质/核酸之间的相互作用以及燃烧等复杂化学体系的反应机理等。近五年来，在Nat. Mach. Intell.、Nat. Commun.、Nucleic Acids Res.、J. Chem. Theory Comput等期刊上发表论文70余篇。他的文章被引用超过1500次。

内容简介：Aviation fuel is a complex mixture with extremely complicated thermal decomposition and combustion reaction pathways. It is difficult to systematically understand the combustion mechanisms of complex kerosene fuels relying solely on current experimental methods and computational techniques. Guessing and providing all possible reaction pathways manually is difficult to achieve, and calculating each reaction pathway using quantum chemistry methods is even more impractical. Driven by the goals of carbon peak and carbon neutrality, people are constantly exploring new low-carbon and zero-carbon fuels, which puts forward a more urgent demand for the efficient construction of high-precision combustion mechanisms. Here we report some recent progress we have made in this direction. By fully integrating machine learning and physical models, we have achieved rapid searching of combustion reaction pathways and fast prediction of rate constants. The introduction of machine learning methods ensures the efficiency of the method, while the physical model fully guarantees its accuracy and extrapolation capability. The development of this method is expected to provide more reliable and efficient tools for the rapid construction of combustion reaction mechanisms.