Literature DB >> 30802325

Extracting the mechanisms and kinetic models of complex reactions from atomistic simulation data.

Yanze Wu1, Huai Sun1, Liang Wu1, Joshua D Deetz1.   

Abstract

Determining reaction mechanisms and kinetic models, which can be used for chemical reaction engineering and design, from atomistic simulation is highly challenging. In this study, we develop a novel methodology to solve this problem. Our approach has three components: (1) a procedure for precisely identifying chemical species and elementary reactions and statistically calculating the reaction rate constants; (2) a reduction method to simplify the complex reaction network into a skeletal network which can be used directly for kinetic modeling; and (3) a deterministic method for validating the derived full and skeletal kinetic models. The methodology is demonstrated by analyzing simulation data of hydrogen combustion. The full reaction network comprises 69 species and 256 reactions, which is reduced into a skeletal network of 9 species and 30 reactions. The kinetic models of both the full and skeletal networks represent the simulation data well. In addition, the essential elementary reactions and their rate constants agree favorably with those obtained experimentally.
© 2019 Wiley Periodicals, Inc. © 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  ReaxFF; directed relation graph; reaction mechanism; reaction model; skeletal mechanism

Year:  2019        PMID: 30802325     DOI: 10.1002/jcc.25809

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  3 in total

1.  Temperature Dependence of Rate Processes Beyond Arrhenius and Eyring: Activation and Transitivity.

Authors:  Valter H Carvalho-Silva; Nayara D Coutinho; Vincenzo Aquilanti
Journal:  Front Chem       Date:  2019-05-29       Impact factor: 5.221

2.  Revealing the Chemical Reaction Properties of a SiHCl3 Pyrolysis System by the ReaxFF Molecular Dynamics Method.

Authors:  Yanping Li; Dazhou Yan; Tao Yang; Guosheng Wen; Xin Yao
Journal:  ACS Omega       Date:  2022-01-28

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

Authors:  Jinzhe Zeng; Liqun Cao; Mingyuan Xu; Tong Zhu; John Z H Zhang
Journal:  Nat Commun       Date:  2020-11-11       Impact factor: 14.919

  3 in total

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