2019/11/27 by Jinzhe Zeng, Liqun Cao, Mingyuan Xu +3
Chemical Engineering · Chemistry · Computer Science · Materials Science · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Benchmark (surveying) #Biological system #Catalysis and Oxidation Reactions #Chemical reaction #Chemistry #Combustion #Computational Drug Discovery Methods #Computational chemistry #Computer science #Machine Learning in Materials Science #Molecular dynamics #Physical chemistry #Physics #Thermodynamics #Work (physics) #cs.LG #physics.chem-ph
paper · pdf · doi:10.1038/s41467-020-19497-z
published as Nat. Commun., 11, 5713 (2020) · Version_01
arxiv created 2019/11/27 · openalex publication_date 2020/11/11 · arxiv updated 2020/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.