| Autor: |
William H. Green, Franklin Goldsmith, Richard West, Katrin Blondal, Emily Mazeau, Nathan Wa-Wai Yee, Kehang Han, Colin Grambow, A. Mark Payne, Agnes Jocher, Mark Goldman, Matthew Johnson, Alon Grinberg Dana, Mengjie Liu |
| Rok vydání: |
2020 |
| DOI: |
10.26434/chemrxiv.13489656 |
| Popis: |
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. One important change is that RMG v3.0 is now Python 3 compatible, which supports the most up-to-date versions of cheminformatics and machine learning packages that RMG depends on. Additionally, 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 also includes parallelization for reaction generation and on-the-fly quantum calculations, and a new molecule isomorphism approach to improve computational performance. 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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