Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification
Name
d2cp05083h.pdf
Description
Published version
Size
2.47 MB
Format
Adobe PDF
Checksum (MD5)
4bf9c72e7b7092c8bf89e29db28a676b
Author(s)
Li, Qiaofeng
Chen, Huaibo
Koenig, Benjamin C
Deng, Sili
Date Issued
February 1, 2023
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
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/d2cp05083h
