Literature DB >> 31829361

ReacNetGenerator: an automatic reaction network generator for reactive molecular dynamics simulations.

Jinzhe Zeng1, Liqun Cao1, Chih-Hao Chin2, Haisheng Ren3, John Z H Zhang4, Tong Zhu2.   

Abstract

Reactive molecular dynamics (MD) simulation makes it possible to study the reaction mechanism of complex reaction systems at the atomic level. However, the analysis of MD trajectories which contain thousands of species and reaction pathways has become a major obstacle to the application of reactive MD simulation in large-scale systems. Here, we report the development and application of the Reaction Network Generator (ReacNetGenerator) method. It can automatically extract the reaction network from the reaction trajectory without any predefined reaction coordinates and elementary reaction steps. Molecular species can be automatically identified from the cartesian coordinates of atoms and the hidden Markov model is used to filter the trajectory noises which makes the analysis process easier and more accurate. The ReacNetGenerator has been successfully used to analyze the reactive MD trajectories of the combustion of methane and 4-component surrogate fuel for rocket propellant 3 (RP-3), and it has great advantages in terms of efficiency and accuracy compared to traditional manual analysis.

Entities:  

Year:  2019        PMID: 31829361     DOI: 10.1039/c9cp05091d

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  2 in total

1.  Ab initio neural network MD simulation of thermal decomposition of a high energy material CL-20/TNT.

Authors:  Liqun Cao; Jinzhe Zeng; Bo Wang; Tong Zhu; John Z H Zhang
Journal:  Phys Chem Chem Phys       Date:  2022-05-18       Impact factor: 3.945

2.  Inhomogeneity Effects on Reactions in Supercritical Fluids: A Computational Study on the Pyrolysis of n-Decane.

Authors:  Yutong Wang; Guozhu Liu
Journal:  JACS Au       Date:  2022-09-06
  2 in total

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