A Machine Learning Based Approach to Reaction Rate Estimation
Name
D3RE00684K.pdf
Size
2.1 MB
Format
Adobe PDF
Checksum (MD5)
6cc0e7feaf897a9a0039f644503f7b26
Author(s)
Johnson, Matthew S.
Green, William H.
Date Issued
February 22, 2024
Journal
Reaction Chemistry & Engineering
Publisher
Royal Society of Chemistry
Citation
React. Chem. Eng., 2024,9, 1364-1380
Version
Final published version
Abstract
Chemical kinetic models are vital to accurately predicting phenomena in a wide variety of fields from combustion to atmospheric chemistry to electrochemistry. However, building an accurate chemical kinetic model requires the efficient and accurate estimation of many reaction rate coefficients for many reaction classes with highly variable amounts of available training data. Current techniques for fast automatic rate estimation tend to be poorly optimized and tedious to maintain and extend. We have developed a machine learning algorithm for automatically training subgraph isomorphic decision trees (SIDT) to predict rate coefficients for arbitrary reaction types. This method is fully automatic, scalable to virtually any dataset size, human readable, can incorporate qualitative chemical knowledge from experts and provides detailed uncertainty information for estimates. The accuracy of the algorithm is tested against the state of the art rate rules scheme in the RMG-database for five selected reaction families. The SIDT method is shown to significantly improve estimation accuracy across all reaction families and considered statistics. The estimator uncertainty estimates are validated against actual errors.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/D3RE00684K
