Different initial data generation strategies are used for each of the data sets mentioned above. For the gas phase or implicit solvated molecules, initial training configurations are generated by randomly displacing the atomic coordinates. In the case of a chemical reaction, For the system in the condensed phase, cluster configurations with a minimum radius (r cluster ) ≥ cut-off hyperparameters (r cut ) in MLP are used as initial configurations. b Selection methods in the AL process: energy selector: selects data points where the energy difference (red) between the ground truth density functional theory (DFT) value (E DFT ) and MLP predicted values (E MLP ) exceeds the threshold (E T ).similarity selector: collects configuration with dissimilar geometry to the original data set, i.e., configuration with the maximum of the similarity vector K lower than similarity threshold k T .distance selector: uses the local outlier factor (LOF) method to identify outliers o from the original data set. The circle with radius k-distance centred at the outlier (o) is highlighted in red.