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

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.

doi:10.1021/acs.jcim.0c01480

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

Journal Article · Fri May 28 00:00:00 EDT 2021 · Journal of Chemical Information and Modeling

DOI:https://doi.org/10.1021/acs.jcim.0c01480· OSTI ID:1851973

^[1]; ^[2]; Johnson, Matthew S. ^[3]; ^[3]; ^[3]; Payne, A. Mark ^[3]; ^[3]; ^[3]; Yee, Nathan W. ^[3]; ^[4]; ^[5]; ^[3]; ^[5]; ^[3]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Chemical Engineering; Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Chemical Engineering
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
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Chemical Engineering
Northeastern Univ., Boston, MA (United States). Dept. of Chemical Engineering
Brown Univ., Providence, RI (United States). School of Engineering

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.

View Accepted Manuscript (DOE)

Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division; USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)

Grant/Contract Number:: SC0014901

OSTI ID:: 1851973

Journal Information:: Journal of Chemical Information and Modeling, Journal Name: Journal of Chemical Information and Modeling Journal Issue: 6 Vol. 61; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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