We have provided a generalized graph neural network modeling framework that can perform surrogate model predictions for NEB energies in arbitrary crystal structures and vacancy migration paths. Relying only on the host structure input and the specification of the end points corresponding to the vacancy migration hop, the model facilitates rapid inference on vacancy thermodynamics (formation energies) and kinetics (migration activation energies), while respecting relevant symmetry constraints thanks to an intermediary Transformer encoder and MLP decoder architecture. For a given material, all predicted activation energies of all paths up to a certain cutoff can be integrated with a firstprinciples, temperature-dependent calculation of the diffusivity tensor. Many materials can then be screened in a high-throughput manner to identify candidates with anomalous/interesting vacancy diffusivity properties. As a proof-of-concept, we conducted high-throughput screening of a large chemical space of metal oxides, combining neutral vacancy defect thermodynamics, kinetics, and host stability, to identify promising candidates for thermochemical hydrogen production via two-step watersplitting redox cycles.