Halide Ion Microhydration: Structure, Energetics, and Spectroscopy of Small Halide–Water Clusters

Abstract

Replica exchange molecular dynamics simulations and vibrational spectroscopy calculations are performed using halide−water many-body potential energy functions to provide a bottom-up analysis of the structures, energetics, and hydrogen-bonding arrangements in X − (H 2 O) n (n = 3−6) clusters, with X = F, Cl, Br, and I. Independently of the cluster size, it is found that all four halides prefer surfacetype structures in which they occupy one of the vertices in the underlying three-dimensional hydrogen-bond networks. For fluoride−water clusters, this is in contrast to previous reports suggesting that fluoride prefers interior-type arrangements, where the ion is fully hydrated. These differences can be ascribed to the variability in how various molecular models are capable of reproducing the subtle interplay between halide−water and water−water interactions. Our results thus emphasize the importance of a correct representation of individual many-body contributions to the molecular interactions for a quantitative description of halide ion hydration.

Highly Accurate Many-Body Potentials for Simulations of N2O5 in Water: Benchmarks, Development, and Validation

“…The first MB-nrg models have been devised to describe the interactions of water with monoatomic ions [57][58][59] and, more recently, to describe CO2/water and CH4/water mixtures. 60,61 Similarly to MB-pol, the MBnrg potential energy functions have been successfully used in studies across phases from clusters [62][63][64][65][66][67] to bulk mixtures. 60,66 It is possible to replace the PIPs with simple atom-pairwise Born-Mayer functions 68,69 to describe the two-body (2B) short-range repulsion between pairs of molecules in an approximate fashion, in which case the model is called TTM-nrg, 70,71 because it is inspired from the Thole-Type Model (TTM).…”

Data-Driven Many-Body Models with Chemical Accuracy for CH4/H2O Mixtures

“…1 to develop TTM-nrg and MB-nrg PEFs for halide-water, 82,83 alkali-metal ion-water, 84,85 and carbon dioxide-water systems, 86 which have been employed in computer simulations of various aqueous systems. [94][95][96][97][98][99][100][101] While both TTM-nrg and MB-nrg PEFs rely on MB-pol to describe water-water interactions, they differ in the functional forms adopted to represent solute distortions and solute-water interactions. In the TTM-nrg PEF, the CH 4 1B term is represented by a functional form similar to those used in common force fields, which is expressed in Eq.…”

General Many-Body Framework for Data-Driven Potentials with Arbitrary Quantum Mechanical Accuracy: Water as a Case Study

“…[33][34][35] An alternative way to represent condensed-phase systems using ML approaches is to adopt a hybrid data-driven/physics-based scheme where a data-driven model, which captures (short-range) quantum-mechanical interactions, is integrated with a physics-based model of many-body interactions, which are represented by classical expressions. [36][37][38] Examples of hybrid data-driven/physicsbased models are the MB-pol and related MB-nrg PEFs that are able to accurately predict the properties of water [39][40][41][42] and various aqueous systems, [43][44][45][46][47][48][49][50] as well as molecular fluids, 51,52 from the gas to condensed phases. Both MB-pol and MB-nrg PEFs are rigorously derived from the many-body expansion (MBE) of the energy and use PIPs 24 to capture short-range quantum-mechanical effects arising from the overlap of the electron densities of individual monomers (e.g., Pauli repulsion, and charge transfer and penetration), which are missing in conventional force fields.…”