Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification-Reference-Cited by-同舟云学术

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification

Published:2023 Issue:5 Volume:25 Page:3707-3717
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Li Qiaofeng¹^ORCID,Chen Huaibo¹,Koenig Benjamin C.¹,Deng Sili¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

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.

Funder

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D2CP05083H

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