Understanding Sorption of Aqueous Electrolytes in Porous Carbon by NMR Spectroscopy.

Ion adsorption at solid-water interfaces is crucial for many electrochemical processes involving aqueous electrolytes including energy storage, electrochemical separations, and electrocatalysis. However, the impact of the hydronium (H3O+) and hydroxide (OH-) ions on the ion adsorption and surface charge distributions remains poorly understood. Many fundamental studies of supercapacitors focus on non-aqueous electrolytes to avoid addressing the role of functional groups and electrolyte pH in altering ion uptake. Achieving microscopic level characterization of interfacial mixed ion adsorption is particularly challenging due to the complex ion dynamics, disordered structures, and hierarchical porosity of the carbon electrodes. This work addresses these challenges starting with pH measurements to quantify the adsorbed H3O+ concentrations, which reveal the basic nature of the activated carbon YP-50F commonly used in supercapacitors. Solid-state NMR spectroscopy is used to study the uptake of lithium bis(trifluoromethanesulfonyl)-imide (LiTFSI) aqueous electrolyte in the YP-50F carbon across the full pH range. The NMR data analysis highlights the importance of including the fast ion-exchange processes for accurate quantification of the adsorbed ions. Under acidic conditions, more TFSI- ions are adsorbed in the carbon pores than Li+ ions, with charge compensation also occurring via H3O+ adsorption. Under neutral and basic conditions, when the carbon's surface charge is close to zero, the Li+ and TFSI- ions exhibit similar but lower affinities toward the carbon pores. Our experimental approach and evidence of H3O+ uptake in pores provide a methodology to relate the local structure to the function and performance in a wide range of materials for energy applications and beyond.

Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes.

Nanoelectrochemical devices have become a promising candidate technology across various applications, including sensing and energy storage, and provide new platforms for studying fundamental properties of electrode/electrolyte interfaces. In this work, we employ constant-potential molecular dynamics simulations to investigate the impedance of gold-aqueous electrolyte nanocapacitors, exploiting a recently introduced fluctuation-dissipation relation. In particular, we relate the frequency-dependent impedance of these nanocapacitors to the complex conductivity of the bulk electrolyte in different regimes, and use this connection to design simple but accurate equivalent circuit models. We show that the electrode/electrolyte interfacial contribution is essentially capacitive and that the electrolyte response is bulk-like even when the interelectrode distance is only a few nanometers, provided that the latter is sufficiently large compared to the Debye screening length. We extensively compare our simulation results with spectroscopy experiments and predictions from analytical theories. In contrast to experiments, direct access in simulations to the ionic and solvent contributions to the polarization allows us to highlight their significant and persistent anticorrelation and to investigate the microscopic origin of the timescales observed in the impedance spectrum. This work opens avenues for the molecular interpretation of impedance measurements, and offers valuable contributions for future developments of accurate coarse-grained representations of confined electrolytes.

Learning intermolecular forces at liquid-vapor interfaces.

By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. We find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated.

Crystal Structure Prediction for Benzene Using Basin-Hopping Global Optimization.

Organic molecules can be stable in distinct crystalline forms, known as polymorphs, which have significant consequences for industrial applications. Here, we predict the polymorphs of crystalline benzene computationally for an accurate anisotropic model parametrized to reproduce electronic structure calculations. We adapt the basin-hopping global optimization procedure to the case of crystalline unit cells, simultaneously optimizing the molecular coordinates and unit cell parameters to locate multiple low-energy structures from a variety of crystal space groups. We rapidly locate all the well-established experimental polymorphs of benzene, each of which corresponds to a single local energy minimum of the model. Our results show that basin-hopping can be both an efficient and effective tool for polymorphic crystal structure prediction, requiring no a priori experimental knowledge of cell parameters or symmetry.

Ion Dissociation Dynamics in an Aqueous Premelting Layer.

Using molecular dynamics simulations and methods of importance sampling, we study the thermodynamics and dynamics of sodium chloride in the aqueous premelting layer formed spontaneously at the interface between ice and its vapor. We uncover a hierarchy of time scales that characterize the relaxation dynamics of this system, spanning the picoseconds of ionic motion to the tens or hundreds of nanoseconds associated with fluctuations of the liquid-crystal interface in their presence. We find that ions distort both local interfaces, incurring restoring forces that result in the ions preferentially residing in the middle of the layer. While ion pair dissociation is thermodynamically favorable, these structural and dynamic effects cause its rate to vary by over an order of magnitude through the layer, with a maximum rate significantly depressed from the corresponding bulk value. The solvation environment of ions in the premelting layer is distinct from that in a bulk liquid, being dominated by slow reorganization of water molecules and a water structure intermediate between ice and its melt.

Optimal Alignment of Structures for Finite and Periodic Systems.

Finding the optimal alignment between two structures is important for identifying the minimum root-mean-square distance (RMSD) between them and as a starting point for calculating pathways. Most current algorithms for aligning structures are stochastic, scale exponentially with the size of structure, and the performance can be unreliable. We present two complementary methods for aligning structures corresponding to isolated clusters of atoms and to condensed matter described by a periodic cubic supercell. The first method (Go-PERMDIST), a branch and bound algorithm, locates the global minimum RMSD deterministically in polynomial time. The run time increases for larger RMSDs. The second method (FASTOVERLAP) is a heuristic algorithm that aligns structures by finding the global maximum kernel correlation between them using fast Fourier transforms (FFTs) and fast SO(3) transforms (SOFTs). For periodic systems, FASTOVERLAP scales with the square of the number of identical atoms in the system, reliably finds the best alignment between structures that are not too distant, and shows significantly better performance than existing algorithms. The expected run time for Go-PERMDIST is longer than FASTOVERLAP for periodic systems. For finite clusters, the FASTOVERLAP algorithm is competitive with existing algorithms. The expected run time for Go-PERMDIST to find the global RMSD between two structures deterministically is generally longer than for existing stochastic algorithms. However, with an earlier exit condition, Go-PERMDIST exhibits similar or better performance.