Critical raw material-free multi-principal alloy design for a net-zero future
Abstract Refractory High-Entropy Alloys (RHEAs), such as NbMoTaW, MoNbTaVW, HfNbTaZr, Re0.1Hf0.25NbTaW0.4, Nb40Ti25Al15V10Ta5Hf3W2, Ti x NbMoTaW (x = 0, 0.25, 0.5, 0.75 and 1), and 3d transition metal HEAs such as Al10.3Co17Cr7.5Fe9Ni48.6Ti5.8Ta0.6Mo0.8W0.4 have demonstrated superior performance com...
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Nature Portfolio
2025-01-01
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| author | Swati Singh Mingwen Bai Allan Matthews Saurav Goel Shrikrishna N. Joshi |
| author_facet | Swati Singh Mingwen Bai Allan Matthews Saurav Goel Shrikrishna N. Joshi |
| author_sort | Swati Singh |
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| description | Abstract Refractory High-Entropy Alloys (RHEAs), such as NbMoTaW, MoNbTaVW, HfNbTaZr, Re0.1Hf0.25NbTaW0.4, Nb40Ti25Al15V10Ta5Hf3W2, Ti x NbMoTaW (x = 0, 0.25, 0.5, 0.75 and 1), and 3d transition metal HEAs such as Al10.3Co17Cr7.5Fe9Ni48.6Ti5.8Ta0.6Mo0.8W0.4 have demonstrated superior performance compared to traditional superalloys, particularly in high-temperature applications for engine components. However, the development of these alloys often depends on critical raw materials (CRMs) such as Ta, W, Nb, Hf, and others. The reliance on critical raw materials (CRMs) not only generates substantial emissions during recycling processes but also imposes considerable risks across global supply chains, hindering the pursuit of Net-zero ambitions. In this pioneering work, we unveil an inventive approach to inversely predict novel multicomponent alloy compositions, meticulously crafted to eliminate CRMs while achieving hardness levels comparable to those of CRM-containing multi-principal element alloys (MPEAs). A robust machine learning (ML) model was developed using a computational database of 3,608 entries, covering unary and binary materials from the Thermo-Calc 2024a software. Among various ML models, the Extra Trees Regressor (ETR) exhibited superior performance and was integrated with metaheuristic optimization techniques to identify novel MPEA compositions. The Cuckoo Search Optimization (CSO) method produced reduced-CRM MPEAs that closely matched Thermo-Calc predictions, with an error margin below ± 20%. To assess the efficacy of these reduced-CRM MPEAs, we compared the hardness of newly synthesized MPEA with CRM-containing counterparts reported in the literature, particularly those with high-risk critical raw materials like Niobium (Nb) and Tantalum (Ta). For example, the CoCrFeNb0.309Ni alloy, which includes CRMs Nb and Co exhibits a Vickers hardness of 480 HV. In contrast, our proposed composition, Ti0.01111NiFe0.4Cu0.4 achieves a comparable hardness of 488 HV without using a CRM. Our objective was not to develop high hardness alloy but to facilitate the development of reduced-CRM multi-principal element alloys (R-CRM-MPEAs). We validated our computational approach through the experimental synthesis of an FCC-phase alloy, Al6.25Cu18.75Fe25Co25Ni25. Thermo-Calc evaluation and ML model predictions of the Vickers hardness showed excellent agreement with the experimental hardness values, which lends credence to our approach. In conclusion, this study provides a robust framework for accelerating the discovery of novel R-CRM-MPEAs, effectively addressing challenges related to supply chain vulnerabilities, import dependence, and related environmental concerns. |
| format | Article |
| id | doaj-art-3e31c57ebaac4e208199b661b460acd9 |
| institution | Kabale University |
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| spelling | doaj-art-3e31c57ebaac4e208199b661b460acd92025-01-26T12:30:41ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-87784-0Critical raw material-free multi-principal alloy design for a net-zero futureSwati Singh0Mingwen Bai1Allan Matthews2Saurav Goel3Shrikrishna N. Joshi4Department of Mechanical Engineering, Indian Institute of Technology GuwahatiSchool of Mechanical Engineering, University of LeedsHenry Royce Institute, The University of ManchesterDepartment of Mechanical Engineering, Indian Institute of Technology GuwahatiDepartment of Mechanical Engineering, Indian Institute of Technology GuwahatiAbstract Refractory High-Entropy Alloys (RHEAs), such as NbMoTaW, MoNbTaVW, HfNbTaZr, Re0.1Hf0.25NbTaW0.4, Nb40Ti25Al15V10Ta5Hf3W2, Ti x NbMoTaW (x = 0, 0.25, 0.5, 0.75 and 1), and 3d transition metal HEAs such as Al10.3Co17Cr7.5Fe9Ni48.6Ti5.8Ta0.6Mo0.8W0.4 have demonstrated superior performance compared to traditional superalloys, particularly in high-temperature applications for engine components. However, the development of these alloys often depends on critical raw materials (CRMs) such as Ta, W, Nb, Hf, and others. The reliance on critical raw materials (CRMs) not only generates substantial emissions during recycling processes but also imposes considerable risks across global supply chains, hindering the pursuit of Net-zero ambitions. In this pioneering work, we unveil an inventive approach to inversely predict novel multicomponent alloy compositions, meticulously crafted to eliminate CRMs while achieving hardness levels comparable to those of CRM-containing multi-principal element alloys (MPEAs). A robust machine learning (ML) model was developed using a computational database of 3,608 entries, covering unary and binary materials from the Thermo-Calc 2024a software. Among various ML models, the Extra Trees Regressor (ETR) exhibited superior performance and was integrated with metaheuristic optimization techniques to identify novel MPEA compositions. The Cuckoo Search Optimization (CSO) method produced reduced-CRM MPEAs that closely matched Thermo-Calc predictions, with an error margin below ± 20%. To assess the efficacy of these reduced-CRM MPEAs, we compared the hardness of newly synthesized MPEA with CRM-containing counterparts reported in the literature, particularly those with high-risk critical raw materials like Niobium (Nb) and Tantalum (Ta). For example, the CoCrFeNb0.309Ni alloy, which includes CRMs Nb and Co exhibits a Vickers hardness of 480 HV. In contrast, our proposed composition, Ti0.01111NiFe0.4Cu0.4 achieves a comparable hardness of 488 HV without using a CRM. Our objective was not to develop high hardness alloy but to facilitate the development of reduced-CRM multi-principal element alloys (R-CRM-MPEAs). We validated our computational approach through the experimental synthesis of an FCC-phase alloy, Al6.25Cu18.75Fe25Co25Ni25. Thermo-Calc evaluation and ML model predictions of the Vickers hardness showed excellent agreement with the experimental hardness values, which lends credence to our approach. In conclusion, this study provides a robust framework for accelerating the discovery of novel R-CRM-MPEAs, effectively addressing challenges related to supply chain vulnerabilities, import dependence, and related environmental concerns.https://doi.org/10.1038/s41598-025-87784-0MPEAsHEAsNet zeroMachine learningCRMs |
| spellingShingle | Swati Singh Mingwen Bai Allan Matthews Saurav Goel Shrikrishna N. Joshi Critical raw material-free multi-principal alloy design for a net-zero future Scientific Reports MPEAs HEAs Net zero Machine learning CRMs |
| title | Critical raw material-free multi-principal alloy design for a net-zero future |
| title_full | Critical raw material-free multi-principal alloy design for a net-zero future |
| title_fullStr | Critical raw material-free multi-principal alloy design for a net-zero future |
| title_full_unstemmed | Critical raw material-free multi-principal alloy design for a net-zero future |
| title_short | Critical raw material-free multi-principal alloy design for a net-zero future |
| title_sort | critical raw material free multi principal alloy design for a net zero future |
| topic | MPEAs HEAs Net zero Machine learning CRMs |
| url | https://doi.org/10.1038/s41598-025-87784-0 |
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