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Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 09 Dec 2019 English Publisher:Royal Society of Chemistry (RSC)Journal:Chemical Science, volume 11, pages 12,464-12,476 (issn: 2041-6520, eissn: 2041-6539, Copyright policy )Funded by:NSF | Wisconsin Materials Resea..., NSF | The SEaRCH: Towards the D...
Authors: Chew, Alex K.; Jiang, Shengli; Zhang, Weiqi; Zavala, Victor M.; Van Lehn, Reid C.;

doi: 10.1039/d0sc03261a , 10.26434/chemrxiv.11299121.v2 , 10.26434/chemrxiv.11299121.v1

pmid: 34094451

pmc: PMC8163029

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

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.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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