Dataset for performance of universal machine learning potentials in global optimization of inorganic crystal structures

This dataset accompanies the manuscript “Performance of universal machine learning potentials in global optimization of inorganic crystal structures.” It provides structural records for merged candidate pools collected from evolutionary optimization workflows together with energetic and metadata records for perturbation tests, including Zn c/a distortions, MB4 phase-stability tests, and Li-B formation-energy calculations. The main database is provided as data/dat.json.gz. The included json query script can read this compressed file directly, so manual extraction is not required. The script can be used to query systems, methods, available energies, atomic positions, POSCAR-format structures, merged-pool structure sets, and Wyckoff information. The archive also contains checksums, the collection manifest, and a README file with usage notes for inspecting and reproducing the collected records.