Under the assumption that this difference is approximately equal for classical force fields and for QM, we combined it with the QM-based CPCM solvent to develop a machine-learned QM implicit solvent, which we denote QM-GNNIS. As a separability of the two contributions is assumed, the benefit of the proposed approach lies in the direct accessibility of energies, gradients, and Hessians necessary to optimize structures and calculate relevant experimental properties. A schematic representation of the approach is shown in Fig. . The force contribution of the solvent on the methoxy oxygen of 2-methoxy-ethanol is indicated. This compound is known to feature a strong explicit-solvent effect with water, as water can form a pseudo-seven-membered ring conformation with the molecule. In these instances, our hypothesis is that the correction force F corr leads to more meaningful solute-solvent interactions than the continuum-based methods on their own.