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

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.

Authors:
ORCiD logo [1]; ORCiD logo [2];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [1]; ORCiD logo [4]; ORCiD logo [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Chemical Engineering
  2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Chemical Engineering; Technion-Israel Institute of Technology, Haifa (Israel). Wolfson Dept. of Chemical Engineering
  3. Northeastern Univ., Boston, MA (United States). Dept. of Chemical Engineering
  4. Brown Univ., Providence, RI (United States). School of Engineering
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division; USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)
OSTI Identifier:
1851973
Grant/Contract Number:  
SC0014901; 122374
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Chemical Information and Modeling
Additional Journal Information:
Journal Volume: 61; Journal Issue: 6; Journal ID: ISSN 1549-9596
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Pharmacology & pharmacy; Chemistry; Computer science; Algorithms; Surface reactions; Molecules; Thermochemistry; Resonance structures

Citation Formats

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, and Green, William H. Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation. United States: N. p., 2021. Web. doi:10.1021/acs.jcim.0c01480.
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. Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation. United States. https://doi.org/10.1021/acs.jcim.0c01480
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, and Green, William H. 2021. "Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation". United States. https://doi.org/10.1021/acs.jcim.0c01480. https://www.osti.gov/servlets/purl/1851973.
@article{osti_1851973,
title = {Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation},
author = {Liu, Mengjie and Grinberg Dana, Alon and Johnson, Matthew S. and Goldman, Mark J. and Jocher, Agnes and Payne, A. Mark and Grambow, Colin A. and Han, Kehang and Yee, Nathan W. and Mazeau, Emily J. and Blondal, Katrin and West, Richard H. and Goldsmith, C. Franklin and Green, William H.},
abstractNote = {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.},
doi = {10.1021/acs.jcim.0c01480},
url = {https://www.osti.gov/biblio/1851973}, journal = {Journal of Chemical Information and Modeling},
issn = {1549-9596},
number = 6,
volume = 61,
place = {United States},
year = {2021},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

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