A solution to the abovementioned limitation could come from recent advances in the development of machine-learned molecular potentials (MLPs). The promise of MLPs is to provide molecular energies at near-QM precision and at near-MM cost. Therefore, by replacing an expensive QM Hamiltonian by a cheap ML alternative, one could reach the microseconds scale with the resulting "ML/MM" MD simulations. Unfortunately, even if a ML potential for the QM region of interest is readily available, it is generally not possible to use it with existing QM/MM engines in a "plug-and-play" manner as one would do with an arbitrary QM Hamiltonian. In state-of-the-art QM/MM, the interaction between QM and MM regions is treated with electrostatic embedding: the polarization of the QM part by the MM environment is treated explicitly, by obtaining the electronic density of the QM part in the presence of the MM point charges. In contrast to QM methods, ML potentials are generally only trained to predict the total energy of the system in vacuo, without considering any external electric fields and without providing a description of the electronic density. Therefore, to employ a pre-trained ML potential in an existing QM/MM implementation, one would have to resort to a much simpler "MM embedding" where the electrostatic interaction between the QM and MM region is based on placing fixed charges on the QM atoms, making it appear as "just another MM region" to the rest of the system. This approach is currently used, for example, to enable ML/MM simulations in OpenMM. Alternatively, one can explicitly train the ML model to predict the QM/MM energies. This allows to go beyond the standard QM/MM, for example, by fixing the artifacts at the boundary between QM and MM regions. However, apart from the need to tailor the ML potential to a given MM environment, these approaches often also go beyond the existing QM/MM architecture, by requiring additional information about the MM environment (beyond the point charges) or by introducing a more complex partitioning of the system. Therefore, these methods require not only retraining of the ML potentials, but also changes in the QM/MM implementation.