The effect of ionic association on the electrochemistry of redox mediators for Li-O batteries: developing a theoretical framework.

A theoretical framework to explain how interactions between redox mediators (RMs) and electrolyte components impact electron transfer kinetics, thermodynamics, and catalytic efficiency is presented. Specifically focusing on ionic association, 2,5-di--butyl-1,4-benzoquinone (DBBQ) is used as a case study to demonstrate these effects. Our analytical equations reveal how the observed redox couple's potential and electron transfer rate constants evolve with Li concentration, resulting from different redox activity mechanisms.

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 (HO) 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.

Scientific Machine Learning of 2D Perovskite Nanosheet Formation.

We apply a scientific machine learning (ML) framework to aid the prediction and understanding of nanomaterial formation processes via a joint spectral-kinetic model. We apply this framework to study the nucleation and growth of two-dimensional (2D) perovskite nanosheets. Colloidal nanomaterials have size-dependent optical properties and can be observed , all of which make them a good model for understanding the complex processes of nucleation, growth, and phase transformation of 2D perovskites.

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.

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.

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.

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.