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Chemical Communications
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
Chemical Communications
Article . 2022
Data sources: Europe PubMed Central
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Sigma profiles in deep learning: towards a universal molecular descriptor

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2022 English Publisher:Royal Society of Chemistry (RSC)Journal:Chemical Communications, volume 58, pages 5,630-5,633 (issn: 1359-7345, eissn: 1364-548X, Copyright policy )
Authors: Dinis O. Abranches; Yong Zhang; Edward J. Maginn; Yamil J. Colón;

doi: 10.1039/d2cc01549h

pmid: 35438096

Sigma profiles in deep learning: towards a universal molecular descriptor

Abstract

Sigma profiles are shown to be universal molecular descriptors in the prediction of material properties using powerful deep learning methodologies.

Related Organizations
Keywords

Deep Learning, Neural Networks, Computer

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    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).
    		 31
    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.
    		 Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Top 1%
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selected citations
These citations are derived from selected sources.
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!
31
Top 10%
Top 10%
Top 1%
hybrid