Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification

Author(s)
Li, Qiaofeng; Chen, Huaibo; Koenig, Benjamin C; Deng, Sili
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Creative Commons Attribution NonCommercial License 3.0 https://creativecommons.org/licenses/by-nc/3.0/
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Abstract
<jats:p>We develop Bayesian Chemical Reaction Neural Network (B-CRNN), a method to infer chemical reaction models and provide the associated uncertainty purely from data without prior knowledge of reaction templates.</jats:p>
Date issued
2023-02-01
URI
https://hdl.handle.net/1721.1/148449
Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Journal
Physical Chemistry Chemical Physics
Publisher
Royal Society of Chemistry (RSC)
Citation
Li, Qiaofeng, Chen, Huaibo, Koenig, Benjamin C and Deng, Sili. 2023. "Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification." Physical Chemistry Chemical Physics, 25 (5).
Version: Final published version
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