Although such ML algorithms can be well-scalable to larger molecules 120 so far no truly large-scale or complex (involving many molecular species) simulation has been conducted. Therefore, it is difficult to assess whether black-box potentials can accurately produce complex biomolecular simulation data in line with experiments, even though they do predict the potential energy surfaces, forces, and vibrational spectra with precision comparable to their training data for small molecules. They might suffer from a similar drawback that the classical force fields optimized using similar QM-based bottom-up approaches seem to have, i.e., not correctly probing the intermolecular interactions. Although subject to the same limitations in the types of systems (transferability and generalizability) and the level of theory used in generating the QM data for the training, 121 black-box potentials do not have the same flexibility for empirical error correction (e.g., by partial re-calibration to new experimental data) as classical force fields.