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

Author

Listed:
  • Jinzhe Zeng

    (East China Normal University)

  • Liqun Cao

    (East China Normal University)

  • Mingyuan Xu

    (East China Normal University)

  • Tong Zhu

    (East China Normal University
    NYU-ECNU Center for Computational Chemistry at NYU Shanghai)

  • John Z. H. Zhang

    (East China Normal University
    NYU-ECNU Center for Computational Chemistry at NYU Shanghai
    New York University
    Shanxi University, Taiyuan)

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.

Suggested Citation

  • Jinzhe Zeng & Liqun Cao & Mingyuan Xu & Tong Zhu & John Z. H. Zhang, 2020. "Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19497-z
    DOI: 10.1038/s41467-020-19497-z
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    Cited by:

    1. Shuai Jiang & Yi-Rong Liu & Teng Huang & Ya-Juan Feng & Chun-Yu Wang & Zhong-Quan Wang & Bin-Jing Ge & Quan-Sheng Liu & Wei-Ran Guang & Wei Huang, 2022. "Towards fully ab initio simulation of atmospheric aerosol nucleation," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    2. Hanwen Zhang & Veronika Juraskova & Fernanda Duarte, 2024. "Modelling chemical processes in explicit solvents with machine learning potentials," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Jing Wu & E Zhou & An Huang & Hongbin Zhang & Ming Hu & Guangzhao Qin, 2024. "Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    4. Jonathan Vandermause & Yu Xie & Jin Soo Lim & Cameron J. Owen & Boris Kozinsky, 2022. "Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    5. Sunghwan Choi, 2023. "Prediction of transition state structures of gas-phase chemical reactions via machine learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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