the training typically starts from the corresponding TS. The initial dataset containing substrate and the explicit solvent can be generated either from solvent molecules in a box under periodic boundary conditions (PBC) or cluster models containing only a handful of solvent molecules placed at relevant positions. While PBC reproduces well the structure of the bulk solvent and includes long-range interactions, generating such training data, in particular with AIMD, is computationally expensive as thousands of configurations are required to accurately describe all the interactions, making it unfeasible for most chemically relevant systems. An alternative approach for generating PBC data is to use classical MM force fields; however, they are often inaccurate and unable to describe bond-breaking/forming processes. Moreover, MM configurations exhibit a weak overlap with the true potential energy surfaces (PES), even for non-reactive systems making them unsuitable for the training of MLPs. In this regard, cluster data labelled with molecular energy and forces provide all structural information for MLPs based on the local descriptors and offer access to a large spectrum of electronic structure methods, including long-range corrected and double-hybrid DFT functionals. The minimum radius of the solvent shell around the substrate should be no less than the cut-off radius used for training the MLP to avoid artificial forces close to the solvent-vacuum interface in the cluster data.