The combination of AL with enhanced sampling methods, such as umbrella sampling (US), metadynamics, and their variants, shows promise for building datasets more efficiently. Uncertainty-driven enhanced sampling techniques have been applied to train MLPs for various systems, such as alloys and polymers, glycine and proton transfer in acetylacetone, and alanine dipeptide and metal-organic frameworks. These techniques improve the accuracy and stability of the generated MLPs by sampling regions of the configuration space with large uncertainty in predicted properties. However, their robustness on reactive systems is dependent on the selection of bias parameters. Several reactive MLPs have been generated to model chemical reactions integrating metadynamics with AL, including reactions in the gas phase (S N 2 51 ), and explicit solvent (urea decomposition in water, oxygen reduction at Au-water interfaces, ring opening of Nenoxyphthalimide, and a Menshutkin reaction ). However, these strategies rely on extensive preliminary exploration of the relevant PES by AIMD. Such high input computational costs still limit their applicability to larger systems.