Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging

Abstract The multi-principal-component concept of high-entropy alloys (HEAs) generates numerous new alloys, among them, nanoscale precipitated HEAs have achieved superior mechanical properties and shown the potentials for structural applications. However, it is still a great challenge to find the optimal alloy within the numerous candidates. Up to now, the reported nanoprecipitated HEAs are mainly designed by a trial-and-error approach with the aid of phase diagram calculations, limiting the development of structural HEAs. In the current work, a novel method is proposed to accelerate the development of ultra-strong nanoprecipitated HEAs. With the guidance of physical metallurgy, the volume fraction of the required nanoprecipitates is designed from a machine learning of big data with thermodynamic foundation while the morphology of precipitates is kinetically tailored by prestrain aging. As a proof-of-principle study, an HEA with superior strength and ductility has been designed and systematically investigated. The newly developed γ′-strengthened HEA exhibits 1.31 GPa yield strength, 1.65 GPa ultimate tensile strength, and 15% tensile elongation. Atom probe tomography and transmission electron microscope characterizations reveal the well-controlled high γ′ volume fraction (52%) and refined precipitate size (19 nm). The refinement of nanoprecipitates originates from the accelerated nucleation of the γ′ phase by prestrain aging. A deeper understanding of the excellent mechanical properties is illustrated from the aspect of strengthening mechanisms. Finally, the versatility of the current design strategy to other precipitation-hardened alloys is discussed.