Name: CeO2_Surface_Oxygen_Vacancy_MLFF Extended ID: CeO2_Surface_Oxygen_Vacancy_MLFF__Oshiro-Gao-Hasegawa__DS_puukty1cgl4q_0 Description: Density functional theory reference data for constructing a machine-learning force field (MLFF) of cerium oxide (CeO2) surfaces containing an oxygen vacancy, generated with VASP on-the-fly machine-learning and stored in ML_AB training files. The dataset follows a dataset-merging strategy, combining six independently sampled surface families that vary the surface orientation (CeO2(100) Ce-terminated and CeO2(111)), slab thickness (two- vs three-layer), and oxygen-vacancy content (zero or one vacancy), for roughly 1,700 configurations carrying total energies, atomic forces, and stresses. Configuration sets group the data by surface family. Reference calculations used VASP with the spin-polarized PBE functional plus Grimme D3 dispersion and a Hubbard U correction on the Ce 4f states (DFT+U, Ueff=5.0 eV), a 520 eV plane-wave cutoff, and a 1x1x1 Gamma-centered k-point grid. Authors: Kai Oshiro Min Gao Jun-ya Hasegawa DOI: None Calculated Property Types: atomic_forces cauchy_stress energy Elements: Ce (33.68%) O (66.32%) Methods: DFT-PBE+U+D3 Software: VASP 6.4.2 Number of Configurations: 1,746 Number of Atoms: 51,361 Publication Link: https://doi.org/10.26434/chemrxiv.15003015/v1 Data Source Link: https://doi.org/10.5281/zenodo.20033455