Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu () and John Z. H. Zhang ()
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Jinzhe Zeng: East China Normal University
Liqun Cao: East China Normal University
Mingyuan Xu: East China Normal University
Tong Zhu: East China Normal University
John Z. H. Zhang: East China Normal University

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.

Date: 2020
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DOI: 10.1038/s41467-020-19497-z

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