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Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.
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Predicting the optimum compositions of high-performance Cu–Zn alloys via machine learning
In the alloy materials, their mechanical properties mightly rely on the compositions and concentrations of chemical elements. Therefore, looking for the optimum elemental concentration and composition is still a critical issue to design high-performance alloy materials. Traditional alloy designing method via “trial and error” or domain experts’ experiences is barely possible to solve the issue. Here, we propose a “composition-oriented” method combined machine learning to design the Cu–Zn alloys with the high strengths, high ductility, and low friction coefficient. The method of separate training for each attribute label is used to study the effects of elemental concentrations on the mechanical properties of Cu–Zn alloys. Moreover, the elemental concentrations of new Cu–Zn alloys with the good mechanical properties are predicted by machine learning. The current results reveal the vital importance of the “composition-oriented” design method via machine learning for the development of high-performance alloys in a broad range of elemental compositions.
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- Award ID(s):
- 1809640
- PAR ID:
- 10265836
- Date Published:
- Journal Name:
- Journal of Materials Research
- Volume:
- 35
- Issue:
- 20
- ISSN:
- 0884-2914
- Page Range / eLocation ID:
- 2709 to 2717
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1557/jmr.2020.258
