ReactionMechanismGenerator / ReactionMechanismGenerator/RMG-database
We should add the new Theoretically Informed Kinetics (ThInK) library
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 127
- Forks
- 160
- Avg merge
- 6d 2h
- Merged PRs (30d)
- 2
Description
Stephen J. Klippenstein, Raghu Sivaramakrishnan, Nicole J. Labbe, Yujie Tao, Michael P. Burke, Sarah N. Elliott, C. Franklin Goldsmith, Clayton R. Mulvihill, Ahren W. Jasper, Branko Ruscic, David H. Bross, Peter Glarborg, Nils Hansen, Judit Zádor, James A. Miller,
Theoretically Informed Kinetics (ThInK): Establishing a modern C0-C3 mechanism for combustion modeling,
Combustion and Flame, Volume 282, (2025), p114501,
https://doi.org/10.1016/j.combustflame.2025.114501.
Abstract: In contrast to the adage “Models are to be used, not believed”, combustion kinetics models have been intended to be predictive in nature. Theoretical chemical kinetics is now understood to provide a firm foundation for the reaction parameters, thereby facilitating predictive simulations of chemical reactivity, even in regimes that are poorly characterized by chemical kinetic and/or combustion experiments. We describe here a theory-informed kinetics model (ThInK) for small molecule combustion chemistry (H2 and C1 – C3 species) that is based on the prodigious use of theoretical predictions for reaction rate coefficients, thermochemistry, and transport parameters. The distinct features of this kinetics model, which was developed over the course of several decades, are illustrated through simulations of flame propagation and auto-ignition.
Novelty and significance statement
The novelty of the “ThInK” C0–C3 mechanism is that an overwhelming number of its parameters are derived from a priori theoretical predictions. It marks a significant departure from traditional models that rely heavily on experimental data and adjusted or empirical parameters. This advancement represents a transformative step in the modeling of combustion kinetics, providing an exceptionally robust small-molecule core mechanism upon which larger models can be based.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue provides the ThInK publication and abstract but names no target files, data format, entry point, or acceptance criteria. Start by identifying how a new kinetics library would be represented in RMG-database and compare that with the paper's C0–C3 mechanism requirements. Done cannot be determined until the required data and validation scope are specified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- database
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100