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【学术预告】Complex Chemical Reactions Simulated by Physics and Machine Learning

发布时间：2022-05-18

阅读：

时间：2022年5月19日（星期四），16:50-18:00

地点：腾讯会议140-966-032

报告题目：Complex Chemical Reactions Simulated by Physics and Machine Learning

报告摘要：

Hydrocarbon molecules are the main components of fuels, and the investigation of their combustion mechanisms is one of the key issues to be addressed in order to simulate engine combustion and thus drive engine design. Due to the limitation of force field accuracy, there is still much room to improve the reliability of the results of the widely used molecular mechanics methods for the simulation of combustion reactions. Due to the large amount of computational resources required for quantum chemical methods, it is not feasible to directly simulate the combustion mechanism of hydrocarbon fuels with them. In our previous work, we have developed a fragment-based quantum chemical calculation method MFCC-combustion, which achieves an efficient and accurate calculation of the energy and force of the simulated system. This method, when combined with MD simulation algorithm, enables ab initio molecular dynamics simulation (AIMD) of fuel combustion through reasonable temperature and pressure control. Recently, we have further improved the simulation efficiency of AIMD by about three orders of magnitude based on the Deep Potential model, thus achieving nanosecond-scale reactive MD simulations of hydrocarbon combustion. The development of this method is expected to provide an efficient and accurate research tool for the understanding of hydrocarbon combustion mechanism and the construction of combustion data base.

报告人简介：

朱通博士2013年毕业于华东师范大学精密光谱科学与技术国家重点实验室。2016-2017年台湾中央研究院访问学者，现为华东师范大学化学与分子工程学院副教授。他的主要研究方向是利用量子力学和分子动力学模拟研究复杂化学系统的结构和性质，包括金属离子与蛋白质/核酸的相互作用以及碳氢燃料的燃烧反应机理。