Nanoparticle strengthening provides a crucial basis for developing high-performance structural materials with potentially superb mechanical properties for structural applications. However, the general wisdom often fails to work well due to the poor thermal stability of nanoparticles, and the rapid coarsening of these particles will lead to the accelerated failures of these materials especially at elevated temperatures. Here, we demonstrate a strategy to achieve ultra-stable nanoparticles at 800~1000 °C in a Ni_59.9-xCo_xFe₁₃Cr₁₅Al₆Ti₆B_0.1 (at.%) chemically complex alloy, resulting from the controllable sluggish lattice diffusion (SLD) effect. Our diffusion kinetic simulations reveal that the Co element leads to a significant reduction in the interdiffusion coefficients of all the main elements, especially for the Al element, with a maximum of up to 5 orders of magnitude. Utilizing first-principles calculations, we further unveil the incompressibility of Al induced by the increased concentration of Co plays a critical role in controlling the SLD effect. These findings are useful for providing advances in the design of novel structural alloys with extraordinary property-microstructure stability combinations for structural applications.

Lightweight refractory high-entropy alloys (LW-RHEAs) hold significant potential in the fields of aviation, aerospace, and nuclear energy due to their low density, high strength, high hardness, and corrosion resistance. However, the enormous composition space has severely hindered the development of novel LW-RHEAs with excellent comprehensive performance. In this paper, an machine learning (ML)-based alloy design strategy combined with a multi-objective optimization method was proposed and applied for a rational design of Al-Nb-Ti-V-Zr-Cr-Mo-Hf LW-RHEAs. The quantitative relation of “composition-structure-property” was first established by ML modeling. Then, feature analysis reveals that Cr content greater than 12 at.% is a key criterion for alloys with high corrosion resistance. The phase structure, density, melting point, hardness and corrosion resistance of the alloys were screened layer by layer, and finally, three LW-RHEAs with superb hard and corrosion resistance were successfully designed. Key experimental validation indicates that three target alloys have densities around 6.5 g/cm³, and all alloys are disordered bcc_A2 single-phase with the highest hardness of 593 HV and the largest pitting potential of 2.5 V_SCE, which far exceeds all the literature reports. The successful demonstration in this paper clearly demonstrates that the present design strategy driven by the ML technique should be generally applicable to other RHEA systems.

High-throughput measurement using the numerical inverse method combined with the diffusion multiple technique was employed to determine the composition-dependent interdiffusion coefficients in fcc Al–Co–Cr–Fe–Ni high-entropy alloys at 1273, 1323, and 1373 K. The reliability of the obtained interdiffusion coefficients was confirmed by comparing the model-predicted composition/interdiffusion flux profiles with the experimental data. The tracer diffusivities of the components were then predicted based on the obtained interdiffusion coefficients and the simplified thermodynamic description. The diagonal interdiffusion coefficients in the present high-entropy alloys were comprehensively compared with those in conventional fcc alloys. Therefore, the sluggish diffusion effect does not apply for all the elements in the present fcc Al–Co–Cr–Fe–Ni high-entropy alloys. A similar result was also observed for the evaluated tracer diffusivities.