High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys

The traditional trial‐and‐error method for designing refractory multi‐principal element alloys (RMPEAs) is inefficient due to a vast compositional design space and high experimental costs. To surmount this challenge, the data‐driven material design based on machine learning (ML) has emerged as a cri...

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Veröffentlicht in:	Materials Genome Engineering Advances Jg. 3; H. 3
Hauptverfasser:	Jin, Chengchen, Xiong, Kai, Luo, Congtao, Fang, Hui, Pu, Chaoguang, Dai, Hua, Zhang, Aimin, Zhang, Shunmeng, Wang, Yingwu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Beijing John Wiley & Sons, Inc 01.09.2025 Wiley-VCH
Schlagworte:	Alloys Approximation Cluster analysis Datasets Decomposition Efficiency elastic properties high‐throughput calculations Integrated approach Machine learning materials design refractory multi‐principal element alloys
ISSN:	2940-9489, 2940-9497
Online-Zugang:	Volltext
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