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.
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