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

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 challe nge to find the optimal alloy within the numerous candidates.Up to now,the reported nanoprecipitated HEAs are mainly designed by a trialand-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 mecha nisms.Finally,the versatility of the current design strategy to other precipitation-hardened alloys is discussed.