The workflow presented here builds on our previous work on an automated AL strategy for reactive MLPs (Fig. ). The AL cycle starts with a training set of around five to ten configurations. These configurations can be obtained by a random displacement of atoms from an input structure, e.g., RS or optimized TS in the gas phase or implicit solvent, or by generating a solvated cluster where solvent molecules are placed around the solute. The structures are then labelled with energies and forces computed at the ground-truth level of theory and used for training the initial version of MLP. Subsequently, the potential is used to propagate several independent MLP-MD trajectories in parallel for n+2 fs, where n is the index of the MD run in the AL loop, starting from zero.