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We present a classical induction model to evaluate the three-body ion–water–water (I–W–W) and water–water–water (W–W–W) interactions in aqueous ionic systems. The classical description of the induction energy is based on electrostatic distributed multipoles up to hexadecapole and distributed polarizabilities up to quadrupole–quadrupole on the O and H atoms of water. The monatomic ions were described by a point charge and a dipole–dipole polarizability, while for the polyatomic ions, distributed multipoles up to hexadecapole and distributed polarizabilities up to quadrupole–quadrupole were used. The accuracy of the classical model is benchmarked against an accurate dataset of 936 (I–W–W) and 2184 (W–W–W) three-body terms for 13 different monatomic and polyatomic cation and anion systems. The classical model shows excellent agreement with the reference second order Moller–Plesset and coupled-cluster single double and perturbative triple [CCSD(T)] three-body energies. The Root-Mean-Square-Errors (RMSEs) for monatomic cations, monatomic anions, and polyatomic ions were 0.29, 0.25, and 0.12 kcal/mol, respectively. The corresponding RMSE for 1744 CCSD(T)/aVTZ three-body (W–W–W) energies, used to train MB-pol, was 0.12 kcal/mol. The accuracy of the proposed classical model demonstrates that the three-body term for aqueous ionic systems can be accurately modeled classically. This approach provides a fast, efficient, and as-accurate path toward modeling the three-body term in aqueous ionic systems that is fully transferable across systems with different ions without the need to fit to tens of thousands of ab initio calculations for each ion to extend existing many-body force fields to interactions between water and ions.

The principal difference between 1-benzyl-3-methyl-imidazolium triflimide [BzC1im][NTf2] and an equimolar mixture of benzene and dimethylimidazolium triflimide [C1C1im][NTf2] is that in the former the benzene moieties are tied to the imidazolium ring, while in the latter they move independently. We use femtosecond optical heterodyne-detected Raman-induced Kerr effect spectroscopy (OHD-RIKES) and molecular simulations to explore some properties of these two systems. The Kerr spectra show small differences in the spectral densities; the simulations also show very similar environments for both the imidazolium rings and the phenyl or benzene parts of the molecules. The low frequency vibrational densities of states are also similar in the model systems. In order to perform the simulations we developed a model for the [BzC1im](+) cation and found that the barriers to rotation of the two parts of the molecule are low.

Creating accurate, analytic atom−atom potentials for small organic molecules from first principles can be a time-consuming and computationally intensive task, particularly if we also require them to include explicit polarization terms, which are essential in many systems. We describe how the CamCASP suite of programs can be used to generate such potentials using some of the most accurate electronic structure methods currently applicable. We derive the long-range terms from monomer properties and determine the short-range anisotropy parameters by a novel and robust method based on the iterated stockholder atom approach. Using these techniques, we develop distributed multipole models for the electrostatic, polarization, and dispersion interactions in the pyridine dimer and develop a series of many-body potentials for the pyridine system. Even the simplest of these potentials exhibits root mean square errors of only about 0.6 kJ mol −1 for the low-energy pyridine dimers, significantly surpassing the best empirical potentials. Our best model is shown to support eight stable minima, four of which have not been reported before in the literature. Further, the functional form can be made systematically more elaborate so as to improve the accuracy without a significant increase in the human-time spent in their generation. We investigate the effects of anisotropy, rank of multipoles, and choice of polarizability and dispersion models.