Enhanced sampling methods enable the efficient calculation of the potential of mean force (PMF) or the FES along the reaction coordinate by accelerating dynamic simulations. Adaptive enhanced sampling methods, such as metadynamics and adaptive biasing force (ABF) , explore the configurational space by adding an adaptively computed bias to the original dynamics. Until recently , these approaches focused on classical systems with few surface chemistry applications. In the last two years, machine learning-assisted enhanced sampling methods have been introduced to accelerate sampling efficiency over existing methods. The combined-force frequency (CFF) approach uses Bayesian regularized artificial neural networks (BRANN) to represent the bias based on the PMF and its gradient, combining several concepts from existing methods, such as metadynamics and ABF. However, the generalizability of these emerging methodologies to reactive systems on materials modeled with electronic structure calculations has not yet been demonstrated.