In particular, the enhanced accuracy and reliability of MLFFs facilitate the application of stochastic algorithms for structural exploration of materials. Stochastic approaches require a substantial volume of calculations and are impracticable with ab-initio methods such as density functional theory (DFT) as the backend. This holds especially true for large and complex systems, such as surface reconstructions of multi-element compounds. Such systems usually feature a complex energy surface with too many degrees of freedom to explore exhaustively, as well as many local minima, where local searches for an optimal structure largely depend on the initial geometry of the search. Given that stochastic searches produce more diverse structures than, e.g., molecular dynamics, a transferable, robust, and generalizable force field trained on a diverse dataset is key to their success.