Published in

Royal Society of Chemistry, Chemical Science, 46(11), p. 12464-12476, 2020

DOI: 10.1039/d0sc03261a

Links

Tools

Export citation

Search in Google Scholar

Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks

Journal article published in 2020 by Alex K. Chew ORCID, Shengli Jiang ORCID, Weiqi Zhang ORCID, Victor M. Zavala ORCID, Reid C. Van Lehn ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Red circle
Preprint: archiving forbidden
Green circle
Postprint: archiving allowed
Upload
Green circle
Published version: archiving allowed
Upload
Policy details
Data provided by SHERPA/RoMEO

Abstract

Solvent-mediated, acid-catalyzed reaction rates relevant to the upgrading of biomass into high-value chemicals are accurately predicted using a combination of molecular dynamics simulations and 3D convolutional neural networks.