A 2-stage iterative approach is used for MLP development. Stage A uses a series of short AIMD simulations (298 K, 2,000 steps, 0.5 fs timestep) of 678 gas-phase adsorbates obtained as their SMILES notation from Reuter et al. (27,000 configurations, Γ-point only) along with AIMD simulations of these adsorbates bound to a constrained 1-layer Ag(111) slab (27,000 configurations). All slab calculations use 4x4x1 k-points with > 10 Å vacuum spacing between periodic images in the z-direction. These AIMD configurations are sampled every 50 steps to obtain an initial DFT training dataset. RDKit 47 and the Atomic Simulation Environment (ASE) are used to streamline the workflows. We emphasize that the 1-layer slab model is physically unrealistic. However, the DFT data from these simulations provides a useful preliminary MLP model (denoted as DP 0 prelim ) to accelerate subsequent active learning iterations in Stage B.