Literature DB >> 34048230

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation.

Mengjie Liu1, Alon Grinberg Dana1,2, Matthew S Johnson1, Mark J Goldman1, Agnes Jocher1, A Mark Payne1, Colin A Grambow1, Kehang Han1, Nathan W Yee1, Emily J Mazeau3, Katrin Blondal4, Richard H West3, C Franklin Goldsmith4, William H Green1.   

Abstract

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

Entities:  

Year:  2021        PMID: 34048230     DOI: 10.1021/acs.jcim.0c01480

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  6 in total

1.  Benchmark calculations for bond dissociation energies and enthalpy of formation of chlorinated and brominated polycyclic aromatic hydrocarbons.

Authors:  Shenying Xu; Quan-De Wang; Mao-Mao Sun; Guoliang Yin; Jinhu Liang
Journal:  RSC Adv       Date:  2021-09-06       Impact factor: 3.361

2.  Scalable reaction network modeling with automatic validation of consistency in Event-B.

Authors:  Usman Sanwal; Thai Son Hoang; Luigia Petre; Ion Petre
Journal:  Sci Rep       Date:  2022-01-25       Impact factor: 4.379

3.  Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis.

Authors:  Miguel Steiner; Markus Reiher
Journal:  Top Catal       Date:  2022-01-13       Impact factor: 2.910

4.  Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining.

Authors:  Mingjian Wen; Samuel M Blau; Xiaowei Xie; Shyam Dwaraknath; Kristin A Persson
Journal:  Chem Sci       Date:  2022-01-11       Impact factor: 9.825

5.  Experimental and Kinetic Modeling Study on High-Temperature Autoignition of Cyclohexene.

Authors:  Jinhu Liang; Fei Li; Shutong Cao; Xiaoliang Li; Ruining He; Ming-Xu Jia; Quan-De Wang
Journal:  ACS Omega       Date:  2022-08-05

6.  High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions.

Authors:  Kevin Spiekermann; Lagnajit Pattanaik; William H Green
Journal:  Sci Data       Date:  2022-07-18       Impact factor: 8.501

  6 in total

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