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.
| Original language | English |
|---|---|
| Pages (from-to) | 2686-2696 |
| Number of pages | 11 |
| Journal | Journal of Chemical Information and Modeling |
| Volume | 61 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 28 2021 |
| Externally published | Yes |
Funding
The authors thank the past and current RMG developers, specifically Connie Gao and Nick Vandewiele for helpful discussions. A.G.D. acknowledges financial support from The Nancy & Stephen Grand Technion Energy Program (GTEP) and from The Mortimer B. Zuckerman STEM Leadership Program. M.J.G. acknowledges financial support from the National Science Foundation Graduate Research Fellowship grant number 1122374. A.J. acknowledges financial support from the DFG Research Fellowship JO 1526/1-1. The primary financial support for the work reported in this paper came from the Gas Phase Chemical Physics Program of the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences (under Award number DE-SC0014901). The work was partially supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award number 0000232253, as part of the Computational Chemical Sciences Program. Additional support by subcontract 7F-30180 to MIT from UC Chicago Argonne LLC is also gratefully acknowledged. It is a component of the Exascale Computing Project (ECP), Project Number 17-SC-20-SC, a collaborative effort of two DOE organizations, the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem including software, applications, hardware, advanced system engineering, and early test bed platforms to support the nation’s exascale computing imperative.