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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Vydáno v:	Materials Genome Engineering Advances Ročník 3; číslo 3
Hlavní autoři:	Jin, Chengchen, Xiong, Kai, Luo, Congtao, Fang, Hui, Pu, Chaoguang, Dai, Hua, Zhang, Aimin, Zhang, Shunmeng, Wang, Yingwu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Beijing John Wiley & Sons, Inc 01.09.2025 Wiley-VCH
Témata:	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
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Abstract	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 critical tool for accelerating materials design. However, the absence of robust datasets impedes the exploitation of machine learning in designing novel RMPEAs. High‐throughput (HTP) calculations have enabled the creation of such datasets. This study addresses these challenges by developing a data‐driven framework for predicting the elastic properties of RMPEAs, integrating HTP calculations with ML. A big dataset of RMPEAs including 4536 compositions was constructed using the new proposed HTP method. A novel stacking ensemble regression algorithm combining multilayer perceptron (MLP) and gradient boosting decision tree (GBDT) was developed, which achieved 92.9% accuracy in predicting the elastic properties of Ti‐V‐Nb‐Ta alloys. Verification experiments confirmed the ML model's accuracy and robustness. This integration of HTP calculations and ML provides a cost‐effective, efficient, and precise alloy design strategy, advancing RMPEAs development. This study effectively integrated high‐throughput calculations and machine learning to accurately predict the elastic properties of Ti‐V‐Nb‐Ta refractory multi‐principal element alloys (RMPEAs), facilitating a more efficient approach to alloy design.
AbstractList	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 critical tool for accelerating materials design. However, the absence of robust datasets impedes the exploitation of machine learning in designing novel RMPEAs. High‐throughput (HTP) calculations have enabled the creation of such datasets. This study addresses these challenges by developing a data‐driven framework for predicting the elastic properties of RMPEAs, integrating HTP calculations with ML. A big dataset of RMPEAs including 4536 compositions was constructed using the new proposed HTP method. A novel stacking ensemble regression algorithm combining multilayer perceptron (MLP) and gradient boosting decision tree (GBDT) was developed, which achieved 92.9% accuracy in predicting the elastic properties of Ti‐V‐Nb‐Ta alloys. Verification experiments confirmed the ML model's accuracy and robustness. This integration of HTP calculations and ML provides a cost‐effective, efficient, and precise alloy design strategy, advancing RMPEAs development. Abstract 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 critical tool for accelerating materials design. However, the absence of robust datasets impedes the exploitation of machine learning in designing novel RMPEAs. High‐throughput (HTP) calculations have enabled the creation of such datasets. This study addresses these challenges by developing a data‐driven framework for predicting the elastic properties of RMPEAs, integrating HTP calculations with ML. A big dataset of RMPEAs including 4536 compositions was constructed using the new proposed HTP method. A novel stacking ensemble regression algorithm combining multilayer perceptron (MLP) and gradient boosting decision tree (GBDT) was developed, which achieved 92.9% accuracy in predicting the elastic properties of Ti‐V‐Nb‐Ta alloys. Verification experiments confirmed the ML model's accuracy and robustness. This integration of HTP calculations and ML provides a cost‐effective, efficient, and precise alloy design strategy, advancing RMPEAs development. 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 critical tool for accelerating materials design. However, the absence of robust datasets impedes the exploitation of machine learning in designing novel RMPEAs. High‐throughput (HTP) calculations have enabled the creation of such datasets. This study addresses these challenges by developing a data‐driven framework for predicting the elastic properties of RMPEAs, integrating HTP calculations with ML. A big dataset of RMPEAs including 4536 compositions was constructed using the new proposed HTP method. A novel stacking ensemble regression algorithm combining multilayer perceptron (MLP) and gradient boosting decision tree (GBDT) was developed, which achieved 92.9% accuracy in predicting the elastic properties of Ti‐V‐Nb‐Ta alloys. Verification experiments confirmed the ML model's accuracy and robustness. This integration of HTP calculations and ML provides a cost‐effective, efficient, and precise alloy design strategy, advancing RMPEAs development. This study effectively integrated high‐throughput calculations and machine learning to accurately predict the elastic properties of Ti‐V‐Nb‐Ta refractory multi‐principal element alloys (RMPEAs), facilitating a more efficient approach to alloy design.
Author	Fang, Hui Dai, Hua Zhang, Shunmeng Jin, Chengchen Xiong, Kai Zhang, Aimin Pu, Chaoguang Wang, Yingwu Luo, Congtao
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Cites_doi	10.1038/s41578-019-0121-4 10.1016/j.jallcom.2023.170543 10.3390/cryst11060612 10.1016/j.msea.2023.145137 10.1016/j.jmrt.2024.04.091 10.1002/zamm.19290090104 10.1038/s43246-024-00487-3 10.1016/j.mser.2021.100645 10.1016/j.msea.2023.145841 10.1016/j.mtcomm.2023.107298 10.1063/5.0021106 10.1038/s41524-021-00645-y 10.1016/j.matdes.2022.110820 10.1007/978-3-030-40245-7_17 10.3390/met14020235 10.3390/sym12061029 10.1016/j.calphad.2005.01.002 10.1126/sciadv.aaz4748 10.1016/j.calphad.2023.102599 10.1002/andp.18892741206 10.1016/j.mtcomm.2019.100858 10.1016/j.pmatsci.2022.101018 10.3389/fmats.2020.00290 10.1039/D0ME00005A 10.1515/ijmr-1942-340303 10.1007/s11837-015-1617-z 10.20517/jmi.2024.15 10.1002/adma.202102401 10.1557/jmr.2018.153 10.1016/j.msea.2024.146401 10.1016/j.actamat.2019.09.026 10.1038/s41578-021-00340-w 10.1039/D4TA00721B 10.1016/j.intermet.2010.05.014 10.1007/s11669-017-0570-7 10.1088/1674-1056/abc0e3 10.1038/s41467-021-24523-9
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Snippet	The traditional trial‐and‐error method for designing refractory multi‐principal element alloys (RMPEAs) is inefficient due to a vast compositional design space... Abstract The traditional trial‐and‐error method for designing refractory multi‐principal element alloys (RMPEAs) is inefficient due to a vast compositional...
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SubjectTerms	Alloys Approximation Cluster analysis Datasets Decomposition Efficiency elastic properties high‐throughput calculations Integrated approach Machine learning materials design refractory multi‐principal element alloys
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Title	High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys
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