Phase prediction and effect of intrinsic residual strain on phase stability in high-entropy alloys with machine learning

The phase formation and stability of high-entropy alloys (HEAs) are crucial to their properties, but the efficient prediction of them remains challenging due to the associated vastness of the composition space. In the present work, we study the formation and stability of solid-solution (SS) phases w...

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Veröffentlicht in:	Journal of alloys and compounds 2022-11, Vol.921, p.166149, Article 166149
Hauptverfasser:	Chang, Huinan, Tao, Yiwen, Liaw, Peter K., Ren, Jingli
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Decision trees High entropy alloys Machine learning Mathematical analysis Multilayer perceptrons Multilayers Parameters Phase prediction Phase stability Phases Physical properties Self organizing maps Solid solutions Support vector machines
Online-Zugang:	Volltext
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