Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases thus constructed rely on the generalized gradient approximation (GGA) for electron exchange and correlation. This limits the reliability of these databases, as well as that of the artificial intelligence (AI) models trained on them, in particular for materials and properties which are not accurately described by GGA. Here, we describe a database of 7,024 inorganic materials presenting diverse structures and compositions. Crucially, the database was generated using hybrid functional calculations,efficiently implemented in the all-electron code FHI-aims. The database is used to evaluate the thermodynamic and electrochemical stability of oxides relevant to catalysis and energy related applications. We illustrate how the database can be used to train AI models for material properties using the sure-independence screening and sparsifying operator (SISSO) approach.