Several outstanding challenges remain and point to exciting areas for improvement in future work. The chemical space across which the model has been trained is limited to 14 cations, so expanding the high-throughput NEB calculations to more diverse (but more challenging for DFT) materials like oxides with 3d transition metals are rare earths will improve its applicability for materials discovery across different domains. Similarly, our DFT training data only consists of NEB trajectories for oxygen vacancy migration, and this could be extended to different element types to again improve applicability in other materials discovery/modeling domains. The current form of the model also only accounts for vacancy migration events occurring between lattice oxygen sites, while hops between interstitial sites are often favorable, and could be accounted for in future data collection and an adjusted modeling framework. As discussed in more detail in Supplementary Section 2, this initial demonstra-tion only focused on neutral oxygen vacancies, which often diffuse more slowly and have higher formation energies than charged vacancies (e.g., V 2+ O ) in materials with band gaps. In such cases, we anticipate that our predictions represent at worst a lower bound on the vacancy diffusivity. Therefore, it would be beneficial in future work to repeat certain calculations with charged vacancies and to estimate the effect of charge on diffusivity predictions in a large-scale screening. Finally, with the advent of machine learning methods that can model potential energy surfaces of inorganic materials across large portions of the periodic table, it will be interesting to compare the relative accuracy of this method to that of NEB calculations performed using machine learned force fields, which may not have been explicitly trained on high energy migration barriers.