Machine learning assisted design of high entropy alloys with desired property

We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value i...

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Veröffentlicht in:	Acta materialia Jg. 170; H. C; S. 109 - 117
Hauptverfasser:	Wen, Cheng, Zhang, Yan, Wang, Changxin, Xue, Dezhen, Bai, Yang, Antonov, Stoichko, Dai, Lanhong, Lookman, Turab, Su, Yanjing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 15.05.2019 Elsevier
Schlagworte:	Active learning Machine learning Materials genome initiative Multi-principal element alloys Materials genome initiative Multi-principal element alloys Active learning Machine learning
ISSN:	1359-6454, 1873-2453
Online-Zugang:	Volltext
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