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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References_xml	– volume: 12 issue: 1 year: 2021 article-title: High‐throughput design of high‐performance lightweight high‐entropy alloys publication-title: Nat Commun – volume: 219 year: 2022 article-title: Elastic behavior of binary and ternary refractory multi‐principal‐element alloys publication-title: Mater Des – volume: 33 start-page: 3092 issue: 19 year: 2018 end-page: 3128 article-title: Development and exploration of refractory high entropy alloys—a review publication-title: J Mater Res – volume: 14 issue: 2 year: 2024 article-title: Machine learning design for high‐entropy alloys: models and algorithms publication-title: Metals – volume: 67 start-page: 2653 issue: 11 year: 2015 end-page: 2669 article-title: Design of refractory high‐entropy alloys publication-title: JOM – volume: 7 issue: 4 year: 2020 article-title: Data‐driven materials research enabled by natural language processing and information extraction publication-title: Appl Phys Rev – volume: 5 start-page: 76 issue: 1 year: 2024 article-title: High‐throughput and data‐driven machine learning techniques for discovering high‐entropy alloys publication-title: Commun Mater – volume: 5 start-page: 725 issue: 4 year: 2020 end-page: 742 article-title: Machine‐learning‐assisted high‐throughput computational screening of high performance metal–organic frameworks publication-title: Mol Syst Des Eng – volume: 12 start-page: 10124 issue: 17 year: 2024 end-page: 10136 article-title: Machine‐learning assisted high‐throughput discovery of solid‐state electrolytes for Li‐ion batteries publication-title: J Mater Chem A – year: 2007 – volume: 898 year: 2024 article-title: Effects of V on the microstructure and mechanical properties of HfNbTaTiVx refractory multi‐principle element alloys: a combined experimental and computational study publication-title: Mater Sci. Eng, A – volume: 1 issue: 1 year: 2023 article-title: Advances in data‐assisted high‐throughput computations for material design publication-title: MGE Adv – volume: 131 year: 2023 article-title: Machine learning for high‐entropy alloys: progress, challenges and opportunities publication-title: Prog Mater Sci – volume: 876 year: 2023 article-title: An experimental and computational design low‐modulus (HfNbTa) 1‐xTix multiprinciple elemental alloys with super formability for biomedical applications publication-title: Mater Sci Eng, A – volume: 7 year: 2020 article-title: High‐throughput calculations for high‐entropy alloys: a brief review publication-title: Front Mater – year: 1984 – volume: 38 start-page: 353 issue: 4 year: 2017 end-page: 368 article-title: TCHEA1: a thermodynamic database not limited for “high entropy” alloys publication-title: J Phase Equilibria Diffus – volume: 37 year: 2023 article-title: Review on applications of artificial neural networks to develop high entropy alloys: a state‐of‐the‐art technique publication-title: Mater Today Commun – volume: 82 year: 2023 article-title: Machine learning aided High‐throughput first‐principles calculations to predict the formation enthalpy of σ phase publication-title: Calphad – volume: 29 start-page: 163 issue: 3 year: 2005 end-page: 211 article-title: Accuracy of ab initio methods in predicting the crystal structures of metals: a review of 80 binary alloys publication-title: Calphad – volume: 12 issue: 6 year: 2020 article-title: Artificial intelligence: an energy efficiency tool for enhanced high performance computing publication-title: Symmetry – volume: 18 start-page: 1758 issue: 9 year: 2010 end-page: 1765 article-title: Refractory high‐entropy alloys publication-title: Intermetallics – volume: 7 start-page: 176 issue: 1 year: 2021 article-title: Prediction of thermoelectric performance for layered IV‐V‐VI semiconductors by high‐throughput ab initio calculations and machine learning publication-title: NPJ Comput Mater – volume: 4 start-page: 515 issue: 8 year: 2019 end-page: 534 article-title: vol. 4, issue 8, High‐entropy alloys publication-title: Nat Rev Mater – volume: 1 issue: 1 year: 2023 article-title: Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology publication-title: MGE Adv – volume: 181 start-page: 124 year: 2019 end-page: 138 article-title: First‐principles and machine learning predictions of elasticity in severely lattice‐distorted high‐entropy alloys with experimental validation publication-title: Acta Mater – volume: 30 start-page: 3886 year: 2024 end-page: 3899 article-title: Effects of Ti content on the tensile ductility, lattice distortion and mechanical properties of VNbTaTix refractory high‐entropy alloys publication-title: J Mater Res Technol – volume: 960 year: 2023 article-title: Accelerated design of high entropy alloys by integrating high throughput calculation and machine learning publication-title: J Alloys Compd – volume: 34 start-page: 70 issue: 3 year: 1942 end-page: 72 article-title: Bemerkungen zur schichtkristallbildung publication-title: Int J Mater Res – volume: 11 issue: 6 year: 2021 article-title: A review of the latest developments in the field of refractory high‐entropy alloys publication-title: Crystals – volume: 147 year: 2022 article-title: Machine‐learning and high‐throughput studies for high‐entropy materials publication-title: Mater Sci Eng, R – volume: 4 issue: 3 year: 2024 article-title: Applications of machine learning method in high‐performance materials design: a review publication-title: J Mater Inf – volume: 6 issue: 37 year: 2020 article-title: Temperature dependence of elastic and plastic deformation behavior of a refractory high‐entropy alloy publication-title: Sci Adv – volume: 9 start-page: 49 issue: 1 year: 1929 end-page: 58 article-title: Berechnung der fließgrenze von mischkristallen auf grund der plastizitätsbedingung für einkristalle publication-title: ZAMM‐J Appl Math Mechanics/Zeitschrift für Angewandte Math und Mech – volume: 889 year: 2024 article-title: An ambient ductile VNbTa refractory medium‐entropy alloy with super rolling formability publication-title: Mater Sci. Eng, A – start-page: 377 year: 2020 end-page: 395 article-title: Database‐driven high‐throughput calculations and machine learning models for materials design publication-title: Mach Learning Meets Quantum Phys – volume: 274 start-page: 573 issue: 12 year: 1889 end-page: 587 article-title: Ueber die Beziehung zwischen den beiden Elasticitätsconstanten isotroper Körper publication-title: Ann Phys – volume: 6 start-page: 730 issue: 8 year: 2021 end-page: 755 article-title: Machine learning for alloys publication-title: Nat Rev Mater – year: 1903 – volume: 29 issue: 11 year: 2020 article-title: Machine learning in materials design: algorithm and application publication-title: Chin Phys B – volume: 33 issue: 48 year: 2021 article-title: Superior high‐temperature strength in a supersaturated refractory high‐entropy alloy publication-title: Adv Mater – volume: 23 year: 2020 article-title: Investigating the deformation mechanisms of a highly metastable high entropy alloy using in‐situ neutron diffraction publication-title: Mater Today Commun – ident: e_1_2_9_5_1 doi: 10.1038/s41578-019-0121-4 – ident: e_1_2_9_24_1 doi: 10.1016/j.jallcom.2023.170543 – volume-title: Computational Quantum Mechanics for Materials Engineers: The EMTO Method and Applications year: 2007 ident: e_1_2_9_32_1 – ident: e_1_2_9_6_1 doi: 10.3390/cryst11060612 – ident: e_1_2_9_10_1 doi: 10.1016/j.msea.2023.145137 – ident: e_1_2_9_8_1 doi: 10.1016/j.jmrt.2024.04.091 – ident: e_1_2_9_41_1 doi: 10.1002/zamm.19290090104 – ident: e_1_2_9_26_1 doi: 10.1038/s43246-024-00487-3 – ident: e_1_2_9_13_1 doi: 10.1016/j.mser.2021.100645 – ident: e_1_2_9_9_1 doi: 10.1016/j.msea.2023.145841 – ident: e_1_2_9_23_1 doi: 10.1016/j.mtcomm.2023.107298 – volume: 1 issue: 1 year: 2023 ident: e_1_2_9_15_1 article-title: Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology publication-title: MGE Adv – ident: e_1_2_9_16_1 doi: 10.1063/5.0021106 – ident: e_1_2_9_28_1 doi: 10.1038/s41524-021-00645-y – ident: e_1_2_9_38_1 doi: 10.1016/j.matdes.2022.110820 – ident: e_1_2_9_12_1 doi: 10.1007/978-3-030-40245-7_17 – ident: e_1_2_9_31_1 doi: 10.3390/met14020235 – ident: e_1_2_9_20_1 doi: 10.3390/sym12061029 – ident: e_1_2_9_17_1 doi: 10.1016/j.calphad.2005.01.002 – ident: e_1_2_9_35_1 doi: 10.1126/sciadv.aaz4748 – ident: e_1_2_9_27_1 doi: 10.1016/j.calphad.2023.102599 – ident: e_1_2_9_40_1 doi: 10.1002/andp.18892741206 – volume-title: The LMTO Method, Vol. 41 of Springer Series in Solid State Sciences year: 1984 ident: e_1_2_9_33_1 – ident: e_1_2_9_36_1 doi: 10.1016/j.mtcomm.2019.100858 – ident: e_1_2_9_21_1 doi: 10.1016/j.pmatsci.2022.101018 – ident: e_1_2_9_19_1 doi: 10.3389/fmats.2020.00290 – ident: e_1_2_9_11_1 doi: 10.1039/D0ME00005A – ident: e_1_2_9_43_1 doi: 10.1515/ijmr-1942-340303 – ident: e_1_2_9_4_1 doi: 10.1007/s11837-015-1617-z – ident: e_1_2_9_29_1 doi: 10.20517/jmi.2024.15 – ident: e_1_2_9_34_1 doi: 10.1002/adma.202102401 – ident: e_1_2_9_2_1 doi: 10.1557/jmr.2018.153 – ident: e_1_2_9_7_1 doi: 10.1016/j.msea.2024.146401 – volume-title: Proceedings of the Physical Society year: 1903 ident: e_1_2_9_42_1 – ident: e_1_2_9_37_1 doi: 10.1016/j.actamat.2019.09.026 – ident: e_1_2_9_22_1 doi: 10.1038/s41578-021-00340-w – ident: e_1_2_9_25_1 doi: 10.1039/D4TA00721B – ident: e_1_2_9_3_1 doi: 10.1016/j.intermet.2010.05.014 – ident: e_1_2_9_39_1 doi: 10.1007/s11669-017-0570-7 – ident: e_1_2_9_30_1 doi: 10.1088/1674-1056/abc0e3 – volume: 1 issue: 1 year: 2023 ident: e_1_2_9_14_1 article-title: Advances in data‐assisted high‐throughput computations for material design publication-title: MGE Adv – ident: e_1_2_9_18_1 doi: 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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