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.

Roles of H-Bonding and Hydride Solvation in the Reaction of Hydrated (Di)electrons with Water to Create H2 and OH.

Even though single hydrated electrons (ehyd-'s) are stable in liquid water, two hydrated electrons can bimolecularly react with water to create H2 and hydroxide: ehyd- + ehyd- + 2H2O → H2 + 2OH-. The rate of this reaction has an unusual temperature and isotope dependence as well as no dependence on ionic strength, which suggests that cosolvation of two electrons as a single hydrated dielectron (e2,hyd2-) might be an important intermediate in the mechanism of this reaction. Here, we present an ab initio density functional theory study of this reaction to better understand the potential properties, reactivity, and experimental accessibility of hydrated dielectrons. Our simulations create hydrated dielectrons by first simulating single ehyd-'s and then injecting a second electron, providing a well-defined time zero for e2,hyd2- formation and offering insight into a potential experimental route to creating dielectrons and optically inducing the reaction. We find that e2,hyd2- immediately forms in every member of our ensemble of trajectories, allowing us to study the molecular mechanism of H2 and OH- formation. The subsequent reaction involves separate proton transfer steps with a generally well-defined hydride subintermediate. The time scales for both proton transfer steps are quite broad, with the first proton transfer step spanning times over a few ps, while the second proton transfer step varies over ∼150 fs. We find that the first proton transfer rate is dictated by whether or not the reacting water is part of an H-bond chain that allows the newly created OH- to rapidly move by Grotthuss-type proton hopping to minimize electrostatic repulsion with H-. The second proton transfer step depends significantly on the degree of solvation of H-, leading to a wide range of reactive geometries where the two waters involved can lie either across the dielectron cavity or more adjacent to each other. This also allows the two proton transfer events to take place either effectively concertedly or sequentially, explaining differing views that have been presented in the literature.

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.