Machine Learning-Aided Design of Materials with Target Elastic Properties
A set of universal descriptors which combines atomic properties with crystal fingerprint are presented to build interpretable models for elastic property prediction. Using the well-performed model, 100 materials with large predicted elastic moduli are screened out and then validated by the first-pri...
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Veröffentlicht in: | Journal of physical chemistry. C 2019-02, Vol.123 (8), p.5042-5047 |
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Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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