Beyond the scope of the MLPs, the variability in the active learning results is also notable. As shown in the results for oxalic acid, even when using the optimal parameters and strategy identified, we found significant variation in the training sets selected. This is especially clear when the active learning was repeated while keeping all parameters except the starting structures constant (Table ). While ML models have inherent variability due to stochastic elements involved in training, considering the most expensive part of developing MLPs is typically in generating the reference data, minimizing variation in the selected training set should be a priority and is worth further study.