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Title: Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

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.
Authors:
 [1] ;  [1] ; ORCiD logo [1] ; ORCiD logo [2]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Northeastern Univ., Boston, MA (United States)
Publication Date:
Grant/Contract Number:
FG02-98ER14914
Type:
Published Article
Journal Name:
Computer Physics Communications
Additional Journal Information:
Journal Volume: 203; Journal Issue: C; Journal ID: ISSN 0010-4655
Publisher:
Elsevier
Research Org:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
OSTI Identifier:
1244562
Alternate Identifier(s):
OSTI ID: 1434632

Gao, Connie W., Allen, Joshua W., Green, William H., and West, Richard H.. Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms. United States: N. p., 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. United States. doi:10.1016/j.cpc.2016.02.013.
Gao, Connie W., Allen, Joshua W., Green, William H., and West, Richard H.. 2016. "Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms". United States. doi: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 = {United States},
year = {2016},
month = {2}
}