In recent years, the application of machine-learned force fields (MLFFs) to materials discovery and structure exploration has markedly increased. Method development and their applications have been advancing in parallel, with innovative and powerful models synergizing with established and proven methods. For example, moving from neural-network force fields that utilize precomputed invariant descriptors to adopting equivariant message passing networks has enabled more data-efficient and transferable MLFFs. Modern applications include foundation models trained on a wide range of materials, 7 transferable water potentials, and condensed phase chemistry. Here, we utilize MLFFs to explore the surface reconstructions of strontium titanate (SrTiO 3 ). This material exemplifies the richness of bulk, surface, and interface properties that can be accessed within a single perovskite material: Donor doping by chemical impurities, oxygen vacancies, or field effects can turn it into an insulator, a metal, a superconductor or even induce confined metallic behaviour in the form of 2D electron gases. The diversity extends to the atomistic details of the surface, where a variety of composition-related, polarity-compensating reconstructions have been found for the (001), (110) and (111) orientations. It is known that specific SrTiO 3 surface reconstructions are difficult to reproduce and can depend on sample history and preparation conditions. Notably, scanning tunneling microscopy (STM) studies of SrTiO 3 surfaces frequently reveal the coexistence and even intermixing of multiple surface structures, a feature common to the surfaces of other complex perovskites such as BaTiO 3 and (La,Sr)MnO 3 . The variety of coexisting surface reconstructions and their dependence on sample history underlines the necessity of changing the framing from identifying a single specific reconstruction to mapping out the range of possible reconstructions. This diversity can serve as an ideal showcase of the power of MLFF-supported stochastic searches.