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
Hauptverfasser: Zeng, Shuming, Li, Geng, Zhao, Yinchang, Wang, Ruirui, Ni, Jun
Format: Artikel
Sprache:eng
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