Finding the Next Superhard Material through Ensemble Learning.
Vickers hardness
ensemble machine learning
high-throughput screening
Journal
Advanced materials (Deerfield Beach, Fla.)
ISSN: 1521-4095
Titre abrégé: Adv Mater
Pays: Germany
ID NLM: 9885358
Informations de publication
Date de publication:
Feb 2021
Feb 2021
Historique:
received:
27
07
2020
revised:
17
10
2020
pubmed:
5
12
2020
medline:
5
12
2020
entrez:
4
12
2020
Statut:
ppublish
Résumé
An ensemble machine-learning method is demonstrated to be capable of finding superhard materials by directly predicting the load-dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load-dependent Vickers hardness data are extracted from the literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving excellent accuracy (R
Identifiants
pubmed: 33274804
doi: 10.1002/adma.202005112
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2005112Subventions
Organisme : University of Houston Division of Research
Organisme : Welch Foundation
ID : E-1981
Organisme : Texas Center for Superconductivity at the University of Houston
Informations de copyright
© 2020 Wiley-VCH GmbH.
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