Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms
Abstract
Reaction Mechanism Generator (RMG) constructs kinetic models composed of elementary chemical reaction steps using a general understanding of how molecules react. Species thermochemistry is estimated through Benson group additivity and reaction rate coefficients are estimated using a database of known rate rules and reaction templates. At its core, RMG relies on two fundamental data structures: graphs and trees. Graphs are used to represent chemical structures, and trees are used to represent thermodynamic and kinetic data. Models are generated using a rate-based algorithm which excludes species from the model based on reaction fluxes. RMG can generate reaction mechanisms for species involving carbon, hydrogen, oxygen, sulfur, and nitrogen. It also has capabilities for estimating transport and solvation properties, and it automatically computes pressure-dependent rate coefficients and identifies chemically-activated reaction paths. RMG is an object-oriented program written in Python, which provides a stable, robust programming architecture for developing an extensible and modular code base with a large suite of unit tests. Computationally intensive functions are cythonized for speed improvements.
- Publication Date:
- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1244562
- Alternate Identifier(s):
- OSTI ID: 1434632
- Grant/Contract Number:
- FG02-98ER14914; 0535604; 0312359
- Resource Type:
- Published Article
- Journal Name:
- Computer Physics Communications
- Additional Journal Information:
- Journal Name: Computer Physics Communications Journal Volume: 203 Journal Issue: C; Journal ID: ISSN 0010-4655
- Publisher:
- Elsevier
- Country of Publication:
- Netherlands
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Gao, Connie W., Allen, Joshua W., Green, William H., and West, Richard H. Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms. Netherlands: N. p., 2016.
Web. doi:10.1016/j.cpc.2016.02.013.
Gao, Connie W., Allen, Joshua W., Green, William H., & West, Richard H. Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms. Netherlands. https://doi.org/10.1016/j.cpc.2016.02.013
Gao, Connie W., Allen, Joshua W., Green, William H., and West, Richard H. Wed .
"Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms". Netherlands. https://doi.org/10.1016/j.cpc.2016.02.013.
@article{osti_1244562,
title = {Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms},
author = {Gao, Connie W. and Allen, Joshua W. and Green, William H. and West, Richard H.},
abstractNote = {Reaction Mechanism Generator (RMG) constructs kinetic models composed of elementary chemical reaction steps using a general understanding of how molecules react. Species thermochemistry is estimated through Benson group additivity and reaction rate coefficients are estimated using a database of known rate rules and reaction templates. At its core, RMG relies on two fundamental data structures: graphs and trees. Graphs are used to represent chemical structures, and trees are used to represent thermodynamic and kinetic data. Models are generated using a rate-based algorithm which excludes species from the model based on reaction fluxes. RMG can generate reaction mechanisms for species involving carbon, hydrogen, oxygen, sulfur, and nitrogen. It also has capabilities for estimating transport and solvation properties, and it automatically computes pressure-dependent rate coefficients and identifies chemically-activated reaction paths. RMG is an object-oriented program written in Python, which provides a stable, robust programming architecture for developing an extensible and modular code base with a large suite of unit tests. Computationally intensive functions are cythonized for speed improvements.},
doi = {10.1016/j.cpc.2016.02.013},
journal = {Computer Physics Communications},
number = C,
volume = 203,
place = {Netherlands},
year = {Wed Jun 01 00:00:00 EDT 2016},
month = {Wed Jun 01 00:00:00 EDT 2016}
}
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https://doi.org/10.1016/j.cpc.2016.02.013
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