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We have studied polarized Au(100) and Au(111) electrodes immersed in electrolyte solution by implementing finite-field methods in density functional theory-based molecular dynamics simulations. This allows us to directly compute the Helmholtz capacitance of electric double layer by including both electronic and ionic degrees of freedom, and the results turn out to be in excellent agreement with experiments. It is found that the electronic response of Au electrode makes a crucial contribution to the high Helmholtz capacitance and the instantaneous adsorption of Cl can lead to a charge inversion on the anodic polarized Au(100) surface. These findings point out ways to improve popular semi-classical models for simulating electrified solid-liquid interfaces and to identify the nature of surface charges therein which are difficult to access in experiments.

Advanced aqueous batteries are promising solutions for grid energy storage. Compared with their organic counterparts, water-based electrolytes enable fast transport kinetics, high safety, low cost, and enhanced environmental sustainability. However, the presence of protons in the electrolyte, generated by the spontaneous ionization of water, may compete with the main charge-storage mechanism, trigger unwanted side reactions, and accelerate the deterioration of the cell performance. Therefore, it is of pivotal importance to understand and master the proton activities in aqueous batteries. This Perspective comments on the following scientific questions: Why are proton activities relevant? What are proton activities? What do we know about proton activities in aqueous batteries? How do we better understand, control, and utilize proton activities?

Electrochemical energy storage and conversion play increasingly important roles in electrification and sustainable development across the globe. A key challenge therein is to understand, control, and design electrochemical energy materials with atomistic precision. This requires inputs from molecular modeling powered by machine learning (ML) techniques. In this work, we have upgraded our pairwise interaction neural network Python package PiNN via introducing equivariant features to the PiNet2 architecture for fitting potential energy surfaces along with PiNet2-dipole for dipole and charge predictions as well as PiNet2-chi for generating atom-condensed charge response kernels. By benchmarking publicly accessible data sets of small molecules, crystalline materials, and liquid electrolytes, we found that the equivariant PiNet2 shows significant improvements over the original PiNet architecture and provides a state-of-the-art overall performance. Furthermore, leveraging on plug-ins such as PiNNAcLe for an adaptive learn-on-the-fly workflow in generating ML potentials and PiNNwall for modeling heterogeneous electrodes under external bias, we expect PiNN to serve as a versatile and high-performing ML-accelerated platform for molecular modeling of electrochemical systems.

Electrochemical energy storage and conversion play increasingly important roles in electrification and sustainable development across the globe. A key challenge therein is to understand, control, and design electrochemical energy materials with atomistic precision. This requires inputs from molecular modeling powered by machine learning (ML) techniques. In this work, we have upgraded our pairwise interaction neural network Python package PiNN via introducing equivariant features to the PiNet2 architecture for fitting potential energy surfaces along with PiNet2-dipole for dipole and charge predictions as well as PiNet2-χ for generating atom-condensed charge response kernels. By benchmarking publicly accessible data sets of small molecules, crystalline materials, and liquid electrolytes, we found that the equivariant PiNet2 shows significant improvements over the original PiNet architecture and provides a state-of-the-art overall performance. Furthermore, leveraging on plug-ins such as PiNNAcLe for an adaptive learn-on-the-fly workflow in generating ML potentials and PiNNwall for modeling heterogeneous electrodes under external bias, we expect PiNN to serve as a versatile and high-performing ML-accelerated platform for molecular modeling of electrochemical systems.

Zinc oxide is a versatile semiconducting metal oxide for both environmental and energy applications. Here, the surface acidity of the ZnO(101<overline>$\bar{1}$0)/NaCl sol. system is investigated by applying density functional theory-based molecular dynamics simulations. A new set of repulsive potential is developed, which leads to a consistent description of pKa of Zn2OsH+. By exploring the relation between the vertical energy gap at the deprotonated state (of acid) and the corresponding pKa, this work reveals that different sets of repulsive potentials are likely needed for accurate predictions of surface acidity for basic oxides.

Water-in-salt electrolytes with a surprisingly large electrochemical stability window of <= 3 V have revived interest in aqueous electrolytes for rechargeable lithium-ion batteries. However, recent reports of acidic pH measured in concentrated electrolyte solutions appear to be in contradiction with the suppressed activity of the hydrogen evolution reaction (HER). Therefore, the fundamental thermodynamics of proton reactivity in concentrated electrolyte solutions remains elusive. In this work, we have used density functional theory-based molecular dynamics (MD) simulations and the proton insertion method to investigate how the HER potential shifts in concentrated LiCl solutions under both acidic and alkaline conditions. Our results show that the intrinsic HER activity increases significantly with the salt concentration under acidic conditions but remains relatively constant under alkaline conditions. Moreover, by leverage over finite-field MD simulations, it is found that a determining factor for the HER activity is the Poisson potential of the liquid phase, which increases in concentrated electrolyte solutions with comparable values from both density functional theory and point-charge models.

Materials that undergo ion-insertion coupled electron transfer are important for energy storage, energy conversion, and optoelectronics applications. Cyclic voltammetry is a powerful technique to understand electrochemical kinetics. However, the interpretation of the kinetic behavior of ion insertion electrodes with analytical solutions developed for ion blocking electrodes has led to confusion about their rate-limiting behavior. The purpose of this manuscript is to demonstrate that the cyclic voltammetry response of thin film electrode materials undergoing solid-solution ion insertion without significant Ohmic polarization can be explained by well-established models for finite diffusion. To do this, we utilize an experimental and simulation approach to understand the kinetics of Li+ insertion-coupled electron transfer into a thin film material (Nb2O5). We demonstrate general trends for the peak current vs scan rate behavior, with the latter parameter elevated to an exponent between limiting values of 1 and 0.5, depending on the solid-state diffusion characteristics of the film (diffusion coefficient, film thickness) and the experiment timescale (scan rate). We also show that values < 0.5 are possible depending on the cathodic potential limit. Our results will be useful to fundamentally understand and guide the selection and design of intercalation materials for multiple applications.

Polymer electrolytes constitute a promising type of material for solid-state batteries. However, one of the bottlenecks for their practical implementation lies in the transport properties, often including restricted Li(+ )self-diffusion and conductivity and low cationic transference numbers. This calls for a molecular understanding of ion transport in polymer electrolytes in which molecular dynamics (MD) simulation can provide both new physical insights and quantitative predictions. Although efforts have been made in this area and qualitative pictures have emerged, direct and quantitative comparisons between experiment and simulation remain challenging because of the lack of a unified theoretical framework to connect them.

In our work, we show that by computing the glass transition temperature (T_g) of the model system and using the normalized inverse temperature 1000/(T - T_g + 50), the Li⁺ self-diffusion coefficient can be compared quantitatively between MD simulations and experiments. This allows us to disentangle the effects of T_g and the polymer dielectric environment on ion conduction in polymer electrolytes, giving rise to the identification of an optimal solvating environment for fast ion conduction.

Unlike Li⁺ self-diffusion coefficients and ionic conductivity, the transference number, which describes the fraction of current carried by Li⁺ ions, depends on the boundary conditions or the reference frame (RF). This creates a non-negligible gap when comparing experiment and simulation because the fluxes in the experimental measurements and in the linear response theory used in MD simulation are defined in different RFs. We show that by employing the Onsager theory of ion transport and applying a proper RF transformation, a much better agreement between experiment and simulation can be achieved for the PEO-LiTFSI system. This further allows us to derive the theoretical expression for the Bruce-Vincent transference number in terms of the Onsager coefficients and make a direct comparison to experiments. Since the Bruce-Vincent method is widely used to extract transference numbers from experimental data, this opens the door to calibrating MD simulations via reproducing the Bruce-Vincent transference number and using MD simulations to predict the true transference number.

In addition, we also address several open questions here such as the time-scale effects on the ion-pairing phenomenon, the consistency check between different types of experiments, the need for more accurate force fields used in MD simulations, and the extension to multicomponent systems. Overall, this Account focuses on building new bridges between experiment and simulation for quantitative comparison, warnings of pitfalls when comparing apples and oranges, and clarifying misconceptions. From a physical chemistry point of view, it connects to concentrated solution theory and provides a unified theoretical framework that can maximize the power of MD simulations. Therefore, this Account will be useful for the electrochemical energy storage community at large and set examples of how to approach experiments from theory and simulation (and vice versa).

Capacitive storage devices allow for fast charge and discharge cycles, making them the perfect complements to batteries for high power applications. Many materials display interesting capacitive properties when they are put in contact with ionic solutions despite their very different structures and (surface) reactivity. Among them, nanocarbons are the most important for practical applications, but many nanomaterials have recently emerged, such as conductive metal-organic frameworks, 2D materials, and a wide variety of metal oxides. These heterogeneous and complex electrode materials are difficult to model with conventional approaches. However, the development of computational methods, the incorporation of machine learning techniques, and the increasing power in high performance computing now allow us to tackle these types of systems. In this Review, we summarize the current efforts in this direction. We show that depending on the nature of the materials and of the charging mechanisms, different methods, or combinations of them, can provide desirable atomic-scale insight on the interactions at play. We mainly focus on two important aspects: (i) the study of ion adsorption in complex nanoporous materials, which require the extension of constant potential molecular dynamics to multicomponent systems, and (ii) the characterization of Faradaic processes in pseudocapacitors, that involves the use of electronic structure-based methods. We also discuss how recently developed simulation methods will allow bridges to be made between double-layer capacitors and pseudocapacitors for future high power electricity storage devices.