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Cited by 319

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

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

Open access
Liu, Mengjie, Grinberg Dana, Alon, Johnson, Matthew S., Goldman, Mark J., Jocher, Agnes, Payne, A. Mark, Grambow, Colin A., Han, Kehang, Yee, Nathan W., Mazeau, Emily J., Blondal, Katrin, West, Richard H., Goldsmith, C. Franklin, Green, William H.·2021·Journal of Chemical Information and Modeling

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.

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Metadata

Authors
Liu, M., Grinberg Dana, A., Johnson, M. S., Goldman, M. J., Jocher, A., Payne, A. M., Grambow, C. A., Han, K., Yee, N. W., Mazeau, E. J., Blondal, K., West, R. H., Goldsmith, C. F., & Green, W. H.
Venue
Journal of Chemical Information and Modeling
Published
2021-05-28
Volume
61
Issue
6
Pages
2686-2696
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
OpenAlex
W3165308978
Cited by
319
References
28
FWCI
22.18
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation. https://app.cochat.ai/papers/10.1021/acs.jcim.0c01480
The paper
Liu, M., Grinberg Dana, A., Johnson, M. S., Goldman, M. J., Jocher, A., Payne, A. M., Grambow, C. A., Han, K., Yee, N. W., Mazeau, E. J., Blondal, K., West, R. H., Goldsmith, C. F., & Green, W. H. (2021). Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.0c01480

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.