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.

翻译：间隙扩散是决定材料在非平衡条件下相稳定性及辐照响应的关键过程。本研究通过结合机器学习（ML）和动力学蒙特卡洛（kMC）方法，研究了Fe-Ni浓缩固溶体合金（CSAs）中的迟缓且具有化学偏向性的间隙扩散，其中ML用于精确高效地实时预测迁移能垒。ML-kMC复现了分子动力学在高温下报告的扩散率。借助这一强大工具，我们发现Fe-Ni合金中观测到的迟缓扩散和“Ni-Ni-Ni”偏向性扩散归因于独特的“势垒锁定”机制，而“Fe-Fe-Fe”偏向性扩散则受“组分主导”机制影响。受上述机理启发，我们提出了一种实用的AvgS-kMC方法，该方法仅依赖迁移模式的平均能垒即可便捷快速地确定间隙介导扩散率。结合AvgS-kMC与差分进化算法，我们应用了一种优化迟缓扩散特性的逆向设计策略，以强调有利迁移模式的关键作用。