The bottleneck for developing data-driven intermolecular potentials concerns the computational inefficiencies associated with diverse training database generation and model hyperparameter tuning. Active learning (AL) has been proposed as an efficient data sampling heuristic and applied to select the most promising training samples from large unlabelled sample pools, which are typically generated from equilibrium MD simulations . However, equilibrium MD based active learning does not appropriately sample meta-stable and out of equilibrium training sub-regions which are necessary for avoiding unphysical traps or numerical instability of the MD simulations . To overcome such limitations, a number of recent studies have employed active learning in combination with enhanced sampling or the direct incorporation of experimentally measured meta-stable structures to improve the diversity of the training databases . Even with a diverse training database, model hyperparameters still need to be carefully tuned to arrive at a model that can provide accurate and numerically stable MD simulations. To this end, data-driven model hyperparameter tuning schemes employing Bayesian optimization, particle swarm, and genetic algo-rithms have been introduced to the community.