Adsorbate complexes with varying N-N bond lengths were equilibrated at finite temperature using 5 ps-long unbiased AIMD simulations, resulting in multiple configurations of N 2 * and 2N* states. These configurations were used to initialize individual walkers in CFF-AIMD and ABF-AIMD simulations. Two CVs (r, Z) were chosen to compute free energies (Figure ). A 2D nonrectangular CV grid is used to avoid unphysical, high energy regions (> 4 eV) (Figure ). CFF-AIMD simulations employed BRANNs with two hidden layers (14 and 10 nodes in the first and second layers, respectively). Our benchmark studies show that the performance is insensitive to the neural network architecture as long the network size is sufficiently large, and the Bayesian regularization reduces the risk of overfitting (see the Supporting Information and Figure ). The FES was evaluated by integrating mean forces over the CV grid to directly compare CFF-AIMD and ABF-AIMD. To provide statistical uncertainties of the PMF, we averaged over three independent enhanced sampling simulations and calculated the standard deviation. Descriptions of CFF-AIMD simulations are in the Supporting Information.