Revealing the crucial role of rough energy landscape on self-diffusion in high-entropy alloys based on machine learning and kinetic Monte Carlo

Most of the outstanding functional and structural performance in high-entropy alloys (HEAs) relates to their sluggish diffusion properties under the rough potential energy landscape (PEL) induced by intrinsic chemical disorder. Due to the highly rugged and multi-dimensional nature of PEL, it is challenging to describe how the diffusion process is controlled by the PEL in HEAs. Here we develop machine learning (ML) models to accurately represent the local atomic environment dependence of PEL in HEAs. By combining the ML model with the kinetic Monte Carlo (kMC) method, we reveal that self-diffusion in HEAs is predominantly governed by the PEL roughness, as characterized by the elemental-specific site energies and migration barriers. Comparisons with previously-proposed simplified models for self-diffusion in HEAs elucidate that the models based on species-averaged migration barriers may be a suitable alternative method to rapidly assess diffusion properties, though the correlation effects may be underestimated. Aided by theoretical analysis, we show that the atomic concentrations of fast-diffusing elements and the differences in the averaged migration barriers for different species are the dominant factors influencing sluggish diffusion in HEAs.