Here, we employ the WTMetaD-IB AL workflow to train an MLP for modelling a similar reaction as in Ref. 67 in DCM, substituting the leaving group from trichloroacetimidate to trichloroacetate to decrease the number of elements in the system. The training dataset comprises three independent subsets generated by WTMetaD-IB AL, each designed to capture different interaction types within the system. Subset 1 consists of 154 substrate configurations in the gas phase. It aims to account for the intrinsic reactivity of the system, ignoring the effects of solvation. Subset 2, initiated from RS solvated with 44 DCM molecules randomly displaced within an 18.5 Å box, contains 245 configurations and targets solvent-solute interactions. Finally, subset 3 includes 166 configurations of 28 DCM molecules within a 12.5 Å box, focusing solely on solvent-solvent interactions in bulk DCM. Data points for subsets 1 and 2 were generated by WTMetaD AL initiated from the RS, eliminating any human bias towards the preferred mechanism. Subset 3 was collected without bias. The combined dataset, comprising 565 configurations (illustrated in Fig. ), was employed to train the final ACE MLP. Further details on the training method are provided in SI §S4.1.