We computed the free energy landscape of a prototypical metal catalyzed reaction, the dissociation of N 2 on Ru(0001), using a neural network-based enhanced sampling method, CFF-AIMD. Our neural network-based method adaptively refines under-visited regions of the FES by estimating the mean forces and frequencies of visits, thus accelerating the enhanced sampling simulations. We find that the PMF for the adsorbed N 2 dissociating on the metal contains two minima with a narrow transition pathway, and that the energetic barriers are consistent with experiments. The predicted enthalpic energy surface shows that a previously reported flat-lying molecularly adsorbed state is absent in the FES, but is present in the PES. This phenomenon demonstrates the value of enhanced sampling methods, which self-consistently identify critical points on the FES, unlike conventional HA approaches, which compute free energies using critical points determined from other PES-based methods. Results from several variants of HA reveal that there are significant deviations in temperature-dependent thermodynamic quantities between CFF-AIMD and the standard HA because of the configurational entropy, thermal averaging of multiple states, the surface atom vibrational modes, and possible anharmonic contributions. Among these effects, the largest contribution to the reaction free energies and barriers is the vibrational entropy of surface atoms. These findings suggest that accelerated AIMD simulations can directly compute the underlying FES that governs the reaction and identify the critical points on the FES required to understand reactions with entropic effects and multiple degrees of freedom.