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- W4376113334 startingPage "111222" @default.
- W4376113334 abstract "A Machine Learning (ML) integrated workflow was utilized to guide the design of Cr, Al-containing five-element high-entropy alloys (HEAs) for achieving an enhanced high-temperature oxidation resistance. ML directs the design of HEAs to a chemical composition consisting of Fe, Cr, Al, Ni, and Cu for enhanced oxidation resistance. The oxidation behavior of AlxCrCuFeNi (x = 0, 0.25, 0.5, 1) HEAs at 1100 °C in air was systematically investigated and the oxidation mechanism was elucidated. The experimental validation agrees well with the ML prediction, demonstrating that ML could be used as a powerful tool for designing alloys with optimized oxidation resistance." @default.
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- W4376113334 date "2023-08-01" @default.
- W4376113334 modified "2023-10-16" @default.
- W4376113334 title "Machine learning-assisted discovery of Cr, Al-containing high-entropy alloys for high oxidation resistance" @default.
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- W4376113334 doi "https://doi.org/10.1016/j.corsci.2023.111222" @default.
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