dc.contributor.author | Li, Qiaofeng | |
dc.contributor.author | Chen, Huaibo | |
dc.contributor.author | Koenig, Benjamin C | |
dc.contributor.author | Deng, Sili | |
dc.date.accessioned | 2023-03-09T19:06:40Z | |
dc.date.available | 2023-03-09T19:06:40Z | |
dc.date.issued | 2023-02-01 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/148449 | |
dc.description.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> | en_US |
dc.language.iso | en | |
dc.publisher | Royal Society of Chemistry (RSC) | en_US |
dc.relation.isversionof | 10.1039/d2cp05083h | en_US |
dc.rights | Creative Commons Attribution NonCommercial License 3.0 | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/3.0/ | en_US |
dc.source | Royal Society of Chemistry (RSC) | en_US |
dc.title | Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification | en_US |
dc.type | Article | en_US |
dc.identifier.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). | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mechanical Engineering | |
dc.relation.journal | Physical Chemistry Chemical Physics | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2023-03-09T18:43:52Z | |
dspace.orderedauthors | Li, Q; Chen, H; Koenig, BC; Deng, S | en_US |
dspace.date.submission | 2023-03-09T18:43:55Z | |
mit.journal.volume | 25 | en_US |
mit.journal.issue | 5 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |