How Ions Break Local Symmetry: Simulations of Polarized Transient Hole Burning for Different Models of the Hydrated Electron in Contact Pairs with Na⁺

The hydrated electron (eaq–) is known via polarized transient hole-burning (pTHB) experiments to have a homogeneously broadened absorption spectrum. Here, we explore via quantum simulation how the pTHB spectroscopy of different eaq– models changes in the presence of electrolytes. The idea is that cation–eaq– pairing can break the local symmetry and, thus, induce persistent inhomogeneity. We find that a “hard” cavity model shows a modest increase in the pTHB recovery time in the presence of salt, while a “soft” cavity model remains homogeneously broadened independent of the salt concentration. We also explore the orientational anisotropy of a fully ab initio density functional theory-based model of the eaq–, which is strongly inhomogeneously broadened without salt and which becomes significantly more inhomogeneously broadened in the presence of salt. The results provide a direct prediction for experiments that can distinguish between different models and, thus, help pin down the hydration structure and dynamics of the eaq–

Does the Traditional Band Picture Describe the Electronic Structure of Doped Conjugated Polymers? TD-DFT and Natural Transition Orbital Study of Doped P3HT

Polarons and bipolarons are created when one or two electrons are removed from the π-system of a p-type conjugated polymer, respectively. In the traditional band picture, the creation of a polaron causes two electronic energy levels to move into the band gap. The removal of a second electron to form a bipolaron causes the two intragap states to move further into the gap. Several groups, however, who looked at the energies of the Kohn–Sham orbitals from DFT calculations, have recently argued that the traditional band picture is incorrect for explaining the spectroscopy of doped conjugated polymers. Instead, the DFT calculations suggest that polaron creation causes only one unoccupied state to move into the band gap near the valence band edge while half-filled state in the valence band and the conduction band bend downward in energy. To understand the discrepancy, we performed TD-DFT calculations of polarons and bipolarons on poly(3-hexylthiophene) (P3HT). Not only do the TD-DFT-calculated absorption spectra match the experimental absorption spectra, but an analysis using natural transitional orbitals (NTOs), which provides an approximate one-electron picture from the many-electron TD-DFT results, supports the traditional band picture. Our TD-DFT/NTO analysis indicates that the traditional band picture also works for bipolarons, a system for which DFT calculations were unable to determine the electronic structure

Short-Range Electron Correlation Stabilizes Noncavity Solvation of the Hydrated Electron

The hydrated electron, <i>e</i>^–_(aq), has often served as a model system to understand the influence of condensed-phase environments on electronic structure and dynamics. Despite over 50 years of study, however, the basic structure of <i>e</i>^–_(aq) is still the subject of controversy. In particular, the structure of <i>e</i>^–_(aq) was long assumed to be an electron localized within a solvent cavity, in a manner similar to halide solvation. Recently, however, we suggested that <i>e</i>^–_(aq) occupies a region of enhanced water density with little or no discernible cavity. The potential we developed was only subtly different from those that give rise to a cavity solvation motif, which suggests that the driving forces for noncavity solvation involve subtle electron-water attractive interactions at close distances. This leads to the question of how dispersion interactions are treated in simulations of the hydrated electron. Most dispersion potentials are <i>ad hoc</i> or are not designed to account for the type of close-contact electron-water overlap that might occur in the condensed phase, and where short-range dynamic electron correlation is important. To address this, in this paper we develop a procedure to calculate the potential energy surface between a single water molecule and an excess electron with high-level CCSD­(T) electronic structure theory. By decomposing the electron-water potential into its constituent energetic contributions, we find that short-range electron correlation provides an attraction of comparable magnitude to the mean-field interactions between the electron and water. Furthermore, we find that by reoptimizing a popular cavity-forming one-electron model potential to better capture these attractive short-range interactions, the enhanced description of correlation predicts a noncavity <i>e</i>^–_(aq) with calculated properties in better agreement with experiment. Although much attention has been placed on the importance of long-range dispersion interactions in water cluster anions, our study reveals that largely unexplored <i>short-range</i> correlation effects are crucial in dictating the solvation structure of the condensed-phase hydrated electron

Ultrafast Studies of Excess Electrons in Liquid Acetonitrile: Revisiting the Solvated Electron/Solvent Dimer Anion Equilibrium

We examine the ultrafast relaxation dynamics of excess electrons injected into liquid acetonitrile using air- and water-free techniques and compare our results to previous work on this system [Xia, C. et al. <i>J. Chem. Phys</i>. <b>2002</b>, <i>117</i>, 8855]. Excess electrons in liquid acetonitrile take on two forms: a “traditional” solvated electron that absorbs in the near-IR, and a solvated molecular dimer anion that absorbs weakly in the visible. We find that excess electrons initially produced via charge-transfer-to-solvent excitation of iodide prefer to localize as solvated electrons, but that there is a subsequent equilibration to form the dimer anion on an ∼80 ps time scale. The spectral signature of this interconversion between the two forms of the excess electron is a clear isosbestic point. The presence of the isosbestic point makes it possible to fully deconvolute the spectra of the two species. We find that solvated molecular anion absorbs quite weakly, with a maximum extinction coefficient of ∼2000 M^–1cm^–1. With the extinction coefficient of the dimer anion in hand, we are also able to determine the equilibrium constant for the two forms of excess electron, and find that the molecular anion is favored by a factor of ∼4. We also find that relatively little geminate recombination takes place, and that the geminate recombination that does take place is essentially complete within the first 20 ps. Finally, we show that the presence of small amounts of water in the acetonitrile can have a fairly large effect on the observed spectral dynamics, explaining the differences between our results and those in previously published work

Time-Resolved Photoelectron Spectroscopy of the Hydrated Electron: Comparing Cavity and Noncavity Models to Experiment

We use nonadiabatic mixed quantum/classical molecular dynamics to simulate recent time-resolved photoelectron spectroscopy (TRPES) experiments on the hydrated electron, and compare the results for both a cavity and a noncavity simulation model to experiment. We find that cavity-model hydrated electrons show an “adiabatic” relaxation mechanism, with ground-state cooling that is fast on the time scale of the internal conversion, a feature that is in contrast to the TRPES experiments. A noncavity hydrated electron model, however, displays a “nonadiabatic” relaxation mechanism, with rapid internal conversion followed by slower ground-state cooling, in good qualitative agreement with experiment. We also show that the experimentally observed early time red shift and loss of anisotropy of the excited-state TRPES peak are consistent with hydrated electron models with homogeneously broadened absorption spectra, but not with those with inhomogeneously broadened absorption spectra. Finally, we find that a decreasing photoionization cross section upon cooling causes the excited-state TRPES peak to decay faster than the underlying radiationless relaxation process, so that the experimentally observed 60–75 fs peak decay corresponds to an actual excited-state lifetime of the hydrated electron that is more likely ∼100 fs

How Does a Solvent Affect Chemical Bonds? Mixed Quantum/Classical Simulations with a Full CI Treatment of the Bonding Electrons

Understanding how a solvent affects the quantum mechanics and reactivity of the chemical bonds of dissolved solutes is of fundamental importance to chemistry. To explore condensed-phase effects on a simple molecular solute, we have studied the six-dimensional two-electron wave function of the bonding electrons of the Na₂ molecule in liquid argon via mixed quantum/classical simulation. We find that even though Ar is an apolar liquid, solvent interactions produce dipole moments on Na₂ that can reach magnitudes over 1.4 D. These interactions also change the selection rules, induce significant motional-narrowing, and cause a large (26 cm⁻¹) blue shift of the dimer’s vibrational spectrum relative to that in the gas phase. These effects cannot be captured via classical simulation, highlighting the importance of quantum many-body effects

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

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

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