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High-entropy alloys (HEAs) prefer to form single-phase solid solutions (body-centered cubic (BCC), face-centered cubic (FCC), or hexagonal closed-packed (HCP)) due to their high mixing entropy. In this paper, we systematically review the mechanical behaviors and properties (such as oxidation and corrosion) of BCC-structured HEAs. The mechanical properties at room temperature and high temperatures of samples prepared by different processes (including vacuum arc-melting, powder sintering and additive manufacturing) are compared, and the effect of alloying on the mechanical properties is analyzed. In addition, the effects of HEA preparation and compositional regulation on corrosion resistance, and the application of high-throughput techniques in the field of HEAs, are discussed. To conclude, alloy development for BCC-structured HEAs is summarized.
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This content will become publicly available on March 31, 2026
Machine-learned molecular modeling of ruthenium: A Kolmogorov-Arnold Network approach
Developing refractory high-entropy superalloys (RSAs) with performance advantages over nickel-based alloys is a critical frontier in materials science. Body-centered cubic (bcc)-based RSAs have attracted significant attention, with ruthenium (Ru) playing a key role in forming two-phase regions of A2 (disordered bcc) + B2 (ordered bcc), which could lead to superalloy-like microstructures. This study introduces the application of the Kolmogorov-Arnold Network (KAN) model to predict the mechanical and thermodynamic properties of Ru while comparing its performance against other commonly used machine-learned models. Utilizing density functional theory calculations as training data, the KAN model demonstrates superior accuracy and computational efficiency compared to conventional methods, while reducing descriptor complexity. The model accurately predicts a range of properties, including elastic constants, thermal expansion coefficients, and various moduli, with discrepancies within 6% of experimental reference data. Molecular dynamics simulations further validate the model’s efficacy, accurately capturing Ru’s phase transitions from hexagonal close-packed (hcp) to face-centered cubic structure and the melting point. This work presents the first application of KAN in materials science, demonstrating how its balanced performance and efficiency provide a new pathway for designing advanced materials, with unique advantages over conventional machine learning approaches in predicting material properties.
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- Award ID(s):
- 2338518
- PAR ID:
- 10615890
- Publisher / Repository:
- AccScience
- Date Published:
- Journal Name:
- International Journal of AI for Materials and Design
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 3041-0746
- Page Range / eLocation ID:
- 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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