How Solvation Alters the Thermodynamics of Asymmetric Bond-Breaking: Quantum Simulation of NaK+ in Liquid Tetrahydrofuran.

Gas-phase potential energy surfaces (PESs) are often used to provide an intuitive understanding of molecular chemical reactivity. Most chemical reactions, however, take place in solution, and it is unclear whether gas-phase PESs accurately represent chemical processes in solvent environments. In this work we use quantum simulations to investigate the dissociation energetics of NaK+ in liquid tetrahydrofuran (THF) to understand the degree to which solvent interactions alter the gas-phase picture. Using umbrella sampling and thermodynamic integration techniques, we construct condensed-phase free energy surfaces of NaK+ on THF in both the ground and electronic excited states. We find that solvation by THF completely alters the nature of the NaK+ bond by reordering the thermodynamic dissociation products. Reaching the thermodynamic dissociation limit in THF also requires a long-range charge transfer process that has no counterpart in the gas phase. Gas-phase PESs, even with perturbations, cannot adequately describe the reactivity of simple asymmetric molecules in solution.

Partial Molar Solvation Volume of the Hydrated Electron Simulated Via DFT.

Different simulation models of the hydrated electron produce different solvation structures, but it has been challenging to determine which simulated solvation structure, if any, is the most comparable to experiment. In a recent work, Neupane et al. [J. Phys. Chem. B 2023, 127, 5941-5947] showed using Kirkwood-Buff theory that the partial molar volume of the hydrated electron, which is known experimentally, can be readily computed from an integral over the simulated electron-water radial distribution function. This provides a sensitive way to directly compare the hydration structure of different simulation models of the hydrated electron with experiment. Here, we compute the partial molar volume of an ab-initio-simulated hydrated electron model based on density-functional theory (DFT) with a hybrid functional at different simulated system sizes. We find that the partial molar volume of the DFT-simulated hydrated electron is not converged with respect to the system size for simulations with up to 128 waters. We show that even at the largest simulation sizes, the partial molar volume of DFT-simulated hydrated electrons is underestimated by a factor of 2 with respect to experiment, and at the standard 64-water size commonly used in the literature, DFT-based simulations underestimate the experimental solvation volume by a factor of ∼3.5. An extrapolation to larger box sizes does predict the experimental partial molar volume correctly; however, larger system sizes than those explored here are currently intractable without the use of machine-learned potentials. These results bring into question what aspects of the predicted hydrated electron radial distribution function, as calculated by DFT-based simulations with the PBEh-D3 functional, deviate from the true solvation structure.

Using Machine Learning to Understand the Causes of Quantum Decoherence in Solution-Phase Bond-Breaking Reactions.

Decoherence is a fundamental phenomenon that occurs when an entangled quantum state interacts with its environment, leading to collapse of the wave function. The inevitability of decoherence provides one of the most intrinsic limits of quantum computing. However, there has been little study of the precise chemical motions from the environment that cause decoherence. Here, we use quantum molecular dynamics simulations to explore the photodissociation of Na2+ in liquid Ar, in which solvent fluctuations induce decoherence and thus determine the products of chemical bond breaking. We use machine learning to characterize the solute-solvent environment as a high-dimensional feature space that allows us to predict when and onto which photofragment the bonding electron will localize. We find that reaching a requisite photofragment separation and experiencing out-of-phase solvent collisions underlie decoherence during chemical bond breaking. Our work highlights the utility of machine learning for interpreting complex solution-phase chemical processes as well as identifies the molecular underpinnings of decoherence.

Solvent Control of Chemical Identity Can Change Photodissociation into Photoisomerization.

In solution-phase chemistry, the solvent is often considered to be merely a medium that allows reacting solutes to encounter each other. In this work, however, we show that moderate locally specific solute-solvent interactions can affect not only the nature of the solute but also the types of reactive chemistry. We use quantum simulation methods to explore how solvent participation in solute chemical identity alters reactions involving the breaking of chemical bonds. In particular, we explore the photoexcitation dynamics of Na2+ dissolved in liquid tetrahydrofuran. In the gas phase, excitation of Na2+ directly leads to dissociation, but in solution, photoexcitation leads to an isomerization reaction involving rearrangement of the first-shell solvent molecules; this isomerization must go to completion before the solute can dissociate. Despite the complexity, the solution-phase reaction dynamics can be captured by a two-dimensional energy surface where one dimension involves only the isomerization of the first-shell solvent molecules.