Hanicinec, Martin;
Mohr, Sebastian;
Tennyson, Jonathan;
(2023)
A Regression Model for Plasma Reaction Kinetics.
Journal of Physics D: Applied Physics
, 56
(7)
, Article 374001. 10.1088/1361-6463/acd390.
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Abstract
Machine learning (ML) is used to provide reactions rates appropriate for models of low temperature plasmas with a focus on A + B $\rightarrow$ C + D binary chemical reactions. The regression model is trained on data extracted from the QBD, KIDA, NFRI and UfDA databases. The regression model used a variety of data on the reactant and product species, some of which also had to be estimated using ML. The final model is a voting regressor comprising three distinct optimized regression models: a support vector regressor, random forest regressor and a gradient-boosted trees regressor model; this model is made freely available via a GitHub repository. As a sample use case, the ML results are used to augment the chemistry of a BCl3/H2 gas mixture.
| Type: | Article |
|---|---|
| Title: | A Regression Model for Plasma Reaction Kinetics |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1361-6463/acd390 |
| Publisher version: | http://dx.doi.org/10.1088/1361-6463/acd390 |
| Language: | English |
| Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by/4.0 Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10171085 |
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