Data mining accelerated the design strategy of high-entropy alloys with the largest hardness based on genetic algorithm optimization

Abstract

This article proposed a design strategy that integrated machine learning models based on random forest and genetic algorithm (GA) for the rapid screening of hardness in the AlCoCrCuFeMoNiTi high-entropy alloys system. Through feature engineering and modeling, valence electron concentration, atomic size difference (δr), Pauling electronegativity difference (Δχ), geometric parameters (Λ), and the Cr content were identified as the five key features in the database. The GA was employed to search for alloys with superior hardness and guided synthesis. After three iterations, the HEA Al₁₈Co₂₁Cr₂₃Fe₂₃Mo₁₅ exhibiting the highest predicted hardness (868.8 HV) was identified. The alloy was predominantly composed of BCC, ordered B2, and σ phases, with an experimental hardness of 899.8 ± 9.9 HV, which as approximately 5.38% greater than the maximum hardness observed in the original dataset. The design strategy can also solve other regression problems and pave the way for optimizing material performance in various engineering applications.