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Abstract Diffusion in alloys is an important class of atomic processes. However, atomistic simulations of diffusion in chemically complex solids are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interest. In this work, long‐timescale simulation methods are developed using reinforcement learning (RL) that extends simulation capability to match the duration of experimental interest. Two special limits, RL transition kinetics simulator (TKS) and RL low‐energy states sampler (LSS), are implemented and explained in detail, while the meaning of general RL are also discussed. As a testbed, hydrogen diffusivity is computed using RL TKS in pure metals and a medium entropy alloy, CrCoNi, and compared with experiments. The algorithm can produce counter‐intuitive hydrogen‐vacancy cooperative motion. We also demonstrate that RL LSS can accelerate the sampling of low‐energy configurations compared to the Metropolis–Hastings algorithm, using hydrogen migration to copper (111) surface as an example. 

The presence of short-range chemical order can be a key factor in determining the mechanical behavior of metals, but directly and unambiguously determining its distribution in complex concentrated alloy systems can be challenging. Here, we directly identify and quantify chemical order in the globally single phase BCC-TiVNbHf(Al) system using aberration corrected scanning transmission electron microscopy (STEM) paired with spatial statistics methods. To overcome the difficulties of short-range order (SRO) quantification with STEM when the components of an alloy exhibit large atomic number differences and near equiatomic ratios, “null hypothesis” tests are used to separate experiment from a random chemical distribution. Experiment is found to deviate from both the case of an ideal random solid solution and a fully ordered structure with statistical significance. We also identify local chemical order in TiVNbHf and confirm and quantify the enhancement of SRO with the addition of Al. These results provide insight into local chemical order in the promising TiVNbHf(Al) refractory alloys while highlighting the utility of spatial statistics in characterizing nanoscale SRO in compositionally complex systems.