  Interstitial diffusion is a pivotal process that governs the phase stability
and irradiation response of materials in non-equilibrium conditions. In this
work, we study sluggish and chemically-biased interstitial diffusion in Fe-Ni
concentrated solid solution alloys (CSAs) by combining machine learning (ML)
and kinetic Monte Carlo (kMC), where ML is used to accurately and efficiently
predict the migration energy barriers on-the-fly. The ML-kMC reproduces the
diffusivity that was reported by molecular dynamics results at high
temperatures. With this powerful tool, we find that the observed sluggish
diffusion and the "Ni-Ni-Ni"-biased diffusion in Fe-Ni alloys are ascribed to a
unique "Barrier Lock" mechanism, whereas the "Fe-Fe-Fe"-biased diffusion is
influenced by a "Component Dominance" mechanism. Inspired by the mentioned
mechanisms, a practical AvgS-kMC method is proposed for conveniently and
swiftly determining interstitial-mediated diffusivity by only relying on the
mean energy barriers of migration patterns. Combining the AvgS-kMC with the
differential evolutionary algorithm, an inverse design strategy for optimizing
sluggish diffusion properties is applied to emphasize the crucial role of
favorable migration patterns.
