Identifying split vacancy defects with machine-learned foundation models and electrostatics (Full Materials Project Screening)

Dataset accompanying the publication "Identifying split vacancy defects with machine-learned foundation models and electrostatics" in JPhys Energy, 2025 (https://doi.org/10.1088/2515-7655/ade916), with individual `json.gz` files for each material investigated (from the Materials Project), containing information on the split vacancy configurations (with site positions, point symmetries, and multiplicities), initial and MLIP-relaxed structures, calculated electrostatic formation energies and more. These can be loaded with `loadfn` from `monty.serialization`, or with `json` etc. These also include the DFT calculated relative energies in applicable cases.Other data files associated with this work are provided at https://zenodo.org/records/14499359 Feel free to contact me (`sk2045[at]cam.ac.uk`) if you have any issues or questions.