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RMG_3_Paper.pdf (1.14 MB)

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

submitted on 26.12.2020, 20:02 and posted on 29.12.2020, 06:46 by Mengjie Liu, Alon Grinberg Dana, Matthew Johnson, Mark Goldman, Agnes Jocher, A. Mark Payne, Colin Grambow, Kehang Han, Nathan Wa-Wai Yee, Emily Mazeau, Katrin Blondal, Richard West, Franklin Goldsmith, William H. Green
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. One important change is that RMG v3.0 is now Python 3 compatible, which supports the most up-to-date versions of cheminformatics and machine learning packages that RMG depends on. Additionally, 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 also includes parallelization for reaction generation and on-the-fly quantum calculations, and a new molecule isomorphism approach to improve computational performance. 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.


Computer-Aided Construction of Chemical Kinetic Models

Basic Energy Sciences

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U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award number 0000232253, as part of the Computational Chemical Sciences Program

Subcontract 7F-30180 to MIT from UC Chicago Argonne LLC, a component of the Exascale Computing Project (ECP), Project Number 17-SC-20-SC, a collaborative effort of two DOE organizations, the Office of Science and the National Nuclear Security Administration

The Nancy & Stephen Grand Technion Energy Program (GTEP)

The Mortimer B. Zuckerman STEM Leadership Program

National Science Foundation Graduate Research Fellowship grant number 1122374

DFG Research Fellowship JO 1526/1–1


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Northeastern University



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Declaration of Conflict of Interest

No conflict of interest

Version Notes

First version, corresponding to initial journal submission.