The prosECCo75 density map (Fig. ) displays a stronger face-on interaction, as expected for a cation-π interaction. Thanks to the improved structural agreement achieved by prosECCo75, these simulations also allow us to predict quantitatively the correlation behavior between H TMA and H Py , as illustrated in Fig. . These plots show even more clearly how prosECCo75 predicts stronger cation-π interactions than CHARMM36. Additional neutron scattering experiments on aqueous solutions of cation-π complexes, planned with isotopic substitutions on the non-exchangable hydrogens, will directly test these predictions. Finally note that a direct consequence of the strenthening of cation-π interactions upon moving from CHARMM36 to prosECCo75 is an increased presence of the neutralizing chloride anions in the vicinity of the aromatic molecules (see Fig. in the SI). Pyridine is infinitely miscible with water at room temperature, making it an excellent candidate for NDIS experiments. However, in our simulations using the CHARMM36 force field, we observe significant aggregation of pyridine even at concentrations well below its solubility limit (2 m), as shown in Fig. and Fig. , pyridine tends to form larger aggregates and also pyridine-pyridine contacts are consistently more favored with CHARMM36. This observation is consistent with previous work that suggests that the CHARMM FF struggles to accurately capture cation Pi interactions.. prosECCo75 largely resolves this issue by effectively incorporating electronic polarizability. As demonstrated by the simulation snapshot and corresponding density maps of twice the bulk density of pyridine-pyridine contacts (Fig. and Fig. ), prosECCo75 significantly reduces the artificial aggregation observed with CHARMM36. This reduction in pyridine-pyridine contacts is mostly due to enhanced competition with pyridine-TMA, cation-π interactions. Although prosECCo75 achieves significantly closer agreement with structure factors from NDIS experiments than CHARMM36, this marks only the beginning of advancing the description of cation-π interactions in FFMD. For example, further refinement of the charge scaled description of cation-π interactions could be explored using machine learning accelerated force field optimization to NDIS data or to more fundamental simulation methods such as AIMD. Finally, further work is required to investigate a wider range of biologically relevant cationic species, such as guanidinium, and other aromatic motifs, like phenol or indole. To this end, we have additionally performed as a first step simulations on the TMACl-phenol system, which is even more directly relevant to biological applications than pyridine since it more closely resembles aromatic side chains of amino acid tyrosine.