Affiliation:
1. Key Laboratory for Corrosion and Protection of The Ministry of Education (MOE) Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China
2. National Materials Corrosion and Protection Data Center Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China
3. Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China
Abstract
AbstractThis 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.