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Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
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....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658983/ https://www.ncbi.nlm.nih.gov/pubmed/33177517 http://dx.doi.org/10.1038/s41467-020-19497-z |
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author | Zeng, Jinzhe Cao, Liqun Xu, Mingyuan Zhu, Tong Zhang, John Z. H. |
author_facet | Zeng, Jinzhe Cao, Liqun Xu, Mingyuan Zhu, Tong Zhang, John Z. H. |
author_sort | Zeng, Jinzhe |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7658983 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76589832020-11-17 Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation Zeng, Jinzhe Cao, Liqun Xu, Mingyuan Zhu, Tong Zhang, John Z. H. Nat Commun Article 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. Nature Publishing Group UK 2020-11-11 /pmc/articles/PMC7658983/ /pubmed/33177517 http://dx.doi.org/10.1038/s41467-020-19497-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zeng, Jinzhe Cao, Liqun Xu, Mingyuan Zhu, Tong Zhang, John Z. H. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation |
title | Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation |
title_full | Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation |
title_fullStr | Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation |
title_full_unstemmed | Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation |
title_short | Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation |
title_sort | complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658983/ https://www.ncbi.nlm.nih.gov/pubmed/33177517 http://dx.doi.org/10.1038/s41467-020-19497-z |
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