Abstract: 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.
Bio: Prof. Zhu graduated with a Ph.D. from the State Key Laboratory of Precision Spectroscopy Science and Technology in 2013, was a visiting scholar at the Academia Sinica in Taiwan from 2016 to 2018, and is now a Professor at the School of Chemistry and Molecular Engineering at East China Normal University. His main research direction is to study the structure and properties of complex chemical systems using quantum calculations and molecular dynamics simulations, including the interaction between metal ions and proteins/nucleic acids and the reaction mechanism of complex chemical systems. In the past five years, he has published more than 70 papers in journals such as Nat. Mach. Intell., Nat. Commun., Nucleic Acids Res., and J. Chem. Theory Comput. His articles have been cited more than 1500 times, including one paper selected as a highly cited paper by Web of Science in 2019.