Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor-Reference-Cited by-同舟云学术

Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor

Published:2023 Issue:3 Volume:8 Page:300-315
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Barnard Trent¹^ORCID,Tseng Steven¹^ORCID,Darby James P.²^ORCID,Bartók Albert P.³^ORCID,Broo Anders⁴^ORCID,Sosso Gabriele C.¹^ORCID

Affiliation:

1. Department of Chemistry, University of Warwick, Coventry, CV4 7AL, UK

2. Department of Engineering, University of Cambridge, Trumpington St., Cambridge, CB2 1PZ, UK

3. Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, UK

4. Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, Pepparedsleden 1, Mölndal SE-431 83, Sweden

Abstract

The smooth overlap of atomic positions (SOAP) descriptor represents an increasingly common approach to encode local atomic environments in a form readily digestible to machine learning algorithms.

Funder

Engineering and Physical Sciences Research Council

European Commission

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/ME/D2ME00149G

Reference63 articles.

1. Advancing Physical Chemistry with Machine Learning

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