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. Program summaryProgram title: RMGCatalogue identifier: AEZW_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEZW_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: MIT/X11 LicenseNo. of lines in distributed program, including test data, etc.: 958681No. of bytes in distributed program, including test data, etc.: 9495441Distribution format: tar.gzProgramming language: Python.Computer: Windows, Ubuntu, and Mac OS computers with relevant compilers.Operating system: Unix/Linux/Windows.RAM: 1 GB minimum, 16 GB or more for larger simulationsClassification: 16.12.External routines: RDKit, Open Babel, DASSL, DASPK, DQED, NumPy, SciPyNature of problem: Automatic generation of chemical kinetic mechanisms for molecules containing C, H, O, S, and N.Solution method: Rate-based algorithm adds most important species and reactions to a model, with rate constants derived from rate rules and other parameters estimated via group additivity methods.Additional comments: The RMG software package also includes CanTherm, a tool for computing the thermodynamic properties of chemical species and both high-pressure-limit and pressure-dependent rate coefficients for chemical reactions using results from quantum chemical calculations. CanTherm is compatible with a variety of ab initio quantum chemistry software programs, including but not limited to Gaussian, MOPAC, QChem, and MOLPRO.Running time: From 30 s for the simplest molecules, to up to several weeks, depending on the size of the molecule and the conditions of the reaction system chosen.
Highlights
Kinetic models are relevant to many chemical processes, including combustion, pyrolysis, and atmospheric oxidation [1]
Numerical solvers and computational chemistry have advanced to the point where detailed kinetic models can be constructed and applied to complex systems
Some detailed kinetic models are constructed by hand, through carefully keeping track of all species and reactions and incorporating relevant chemistry
Summary
Kinetic models are relevant to many chemical processes, including combustion, pyrolysis, and atmospheric oxidation [1]. Some detailed kinetic models are constructed by hand, through carefully keeping track of all species and reactions and incorporating relevant chemistry. This process is often tedious and error-prone, requiring expert and up-to-date understanding of chemistry. The challenges associated with handconstructed models are the very things that are handled by computers This insight has spawned several automatic reaction mechanism generation codes, some proprietary and some open-source, including MAMOX, NetGen, REACTION, and EXGAS. In 2008, Joshua Allen and Richard West began writing a Python version of RMG, known as RMG-Py [13] This was motivated by improved code readability, better error handling, and broader access to a variety of existing chem informatics libraries. This paper presents the features and usage of the current Python version of RMG
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
