Show simple item record

dc.contributor.authorLi, Qiaofeng
dc.contributor.authorChen, Huaibo
dc.contributor.authorKoenig, Benjamin C
dc.contributor.authorDeng, Sili
dc.date.accessioned2023-03-09T19:06:40Z
dc.date.available2023-03-09T19:06:40Z
dc.date.issued2023-02-01
dc.identifier.urihttps://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.isoen
dc.publisherRoyal Society of Chemistry (RSC)en_US
dc.relation.isversionof10.1039/d2cp05083hen_US
dc.rightsCreative Commons Attribution NonCommercial License 3.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/en_US
dc.sourceRoyal Society of Chemistry (RSC)en_US
dc.titleBayesian chemical reaction neural network for autonomous kinetic uncertainty quantificationen_US
dc.typeArticleen_US
dc.identifier.citationLi, 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.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.relation.journalPhysical Chemistry Chemical Physicsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-03-09T18:43:52Z
dspace.orderedauthorsLi, Q; Chen, H; Koenig, BC; Deng, Sen_US
dspace.date.submission2023-03-09T18:43:55Z
mit.journal.volume25en_US
mit.journal.issue5en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record