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Explaining and predicting the distributionof ions at the air–waterinterface has been a central challenge of physical chemistry for nearlya century. In essence, the problem amounts to calculating the changein the solvation energy of an ion as it approaches the interface.Here, we generalize our recently developed model of ionic solvationenergies to calculate this interaction. The change in the Born energyas well as the static polarization response of the ion is includedby using the conductor-like screening model (COSMO), which treatsthe ions quantum mechanically. Approximate expressions for the dispersionrepulsion, cavity attraction, and surface potential contributionsare also included. This model reproduces the surface tensions of electrolytesolutions and is consistent with ab initio molecular dynamics (MD)simulation. The model provides clear physical insight into iodide’sadsorption. Unlike alternative models, no parameters are deliberatelyadjusted to reproduce surface tensions, and all of the important contributionsto the interactions are included. Solving this problem has importantdirect implications for atmospheric chemistry and bubble properties.It also has important indirect implications for the more complex interactionsof ions with protein and mineral surfaces. These play a fundamentalrole in a vast number of biological and industrial processes. Themodel is conceptually simple and has low computational demand, whichfacilitates its extension to these important applications. [ABSTRACT FROM AUTHOR]

Continuum solvent models of electrolytesolutions are extremelyuseful. However, before we can use them with confidence, it is importantto test them by comparison with a range of experimental properties.Here, we have adapted our recently developed1,2 simplecontinuum solvent model of ionic solvation free energies to calculatethe solvation entropies and partial molar volumes of a group of monovalentand monatomic ions. This procedure gives good quantitative agreementfor larger ions, and reproduces key qualitative features, such asthe shift to positive entropies of solvation for iodide and the shiftto negative partial molar volumes for small cations. Small ions requirea correction to account for dielectric saturation effects, which bringsthem also into good agreement with experiment. We argue that thismodel does not require ad hoc corrections, and uses parameters thathave good external physical justification. This work therefore establishesthat our continuum solvent model can provide a satisfactory understandingof ionic solvation. It can thus serve as a foundation for improvedmodels that explain and predict more complex ion specific effects. [ABSTRACT FROM AUTHOR]

The dispersion energy is an importantcontribution to the total solvation energies of ions and neutral molecules.Here, we present a new continuum model calculation of these energies,based on macroscopic quantum electrodynamics. The model uses the frequencydependent multipole polarizabilities of molecules in order to accuratelycalculate the dispersion interaction of a solute particle with surroundingwater molecules. It includes the dipole, quadrupole, and octupolemoment contributions. The water is modeled via a bulk dielectric susceptibilitywith a spherical cavity occupied by the solute. The model invokesdamping functions to account for solute–solvent wave functionoverlap. The assumptions made are very similar to those used in theBorn model. This provides consistency and additivity of electrostaticand dispersion (quantum mechanical) interactions. The energy increasesin magnitude with cation size, but decreases slightly with size forthe highly polarizable anions. The higher order multipole momentsare essential, making up more than 50% of the dispersion solvationenergy of the fluoride ion. This method provides an accurate and simpleway of calculating the notoriously problematic dispersion contributionto the solvation energy. The result establishes the importance ofusing accurate calculations of the dispersion energy for the modelingof solvation. [ABSTRACT FROM AUTHOR]

Physically accurate continuum solventmodels that can calculate solvation energies are crucial to explainand predict the behavior of solute particles in water. Here, we presentsuch a model applied to small spherical ions and neutral atoms. Itimproves upon a basic Born electrostatic model by including a standardcavity energy and adding a dispersion component, consistent with theBorn electrostatic energy and using the same cavity size parameter.We show that the well-known, puzzling differences between the solvationenergies of ions of the same size is attributable to the neglecteddispersion contribution. This depends on dynamic polarizability aswell as size. Generally, a large cancellation exists between the cavityand dispersion contributions. This explains the surprising successof the Born model. The model accurately reproduces the solvation energiesof the alkali halide ions, as well as the silver(I) and copper(I)ions with an error of 12 kJ mol–1(±3%). Thesolvation energy of the noble gases is also reproduced with an errorof 2.6 kJ mol–1(±30%). No arbitrary fittingparameters are needed to achieve this. This model significantly improvesour understanding of ionic solvation and forms a solid basis for theinvestigation of other ion-specific effects using a continuum solventmodel. [ABSTRACT FROM AUTHOR]

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are a breakthrough new tool that are already enabling us to simulate systems at the molecular scale with unprecedented accuracy and speed, relying on nothing but fundamental physical laws. The continued development of this approach will realize Paul Dirac's 80-year-old vision of using quantum mechanics to unify physics with chemistry and providing invaluable tools for understanding materials science, biology, earth sciences, and beyond. The era of highly accurate and efficient first-principles molecular simulations will provide a wealth of training data that can be used to build automated computational methodologies, using tools such as diffusion models, for the design and optimization of systems at the molecular scale. Large language models (LLMs) will also evolve into increasingly indispensable tools for literature review, coding, idea generation, and scientific writing., Competing Interests: The author declares no competing financial interest., (© 2024 The Author. Published by American Chemical Society.)