While the free energies of surface reactions at finite temperature and pressure can be directly calculated from dynamical simulations using DFT, i.e., ab initio molecular dynamics (AIMD), these simulations are computationally demanding (e.g., several hundred electrons) and limited to short-time scales (e.g., tens to hundreds of picoseconds). Recent works on N 2 on Ru and other molecule-surface reactions have circumvented this problem by replacing DFT with neural networks and other machine-learning-based PES or empirical interatomic potentials . Here we develop and apply an enhanced sampling approach to map the free energy landscape of N 2 undergoing molecular dissociation on Ru using AIMD. In this approach, we compute potential energies and forces directly from DFT, so the PES part of the free energy surface (FES) is constructed by performing "on-the-fly" DFT calculations.