Computational Ag/AgCl Reference Electrode from Density Functional Theory-Based Molecular Dynamics

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Here, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.

Understanding electrochemical interfaces through comparing experimental and computational charge density-potential curves

Electrode-electrolyte interfaces play a decisive role in electrochemical charge accumulation and transfer processes. Theoretical modelling of these interfaces is critical to decipher the microscopic details of such phenomena. Different force field-based molecular dynamics protocols are compared here in a view to connect calculated and experimental charge density-potential relationships. Platinum-aqueous electrolyte interfaces are taken as a model. The potential of using experimental charge density-potential curves to transform cell voltage into electrode potential in force-field molecular dynamics simulations, and the need for that purpose of developing simulation protocols that can accurately calculate the double-layer capacitance, are discussed.

The hydrogen-bonding dynamics of water to a nitrile-functionalized electrode is modulated by voltage according to ultrafast 2D IR spectroscopy

We report the hydrogen-bonding dynamics of water to a nitrile-functionalized and plasmonic electrode surface as a function of applied voltage. The surface-enhanced two-dimensional infrared spectra exhibit hydrogen-bonded and non-hydrogen-bonded nitrile features in similar proportions, plus cross peaks between the two. Isotopic dilution experiments show that the cross peaks arise predominantly from chemical exchange between hydrogen-bonded and non-hydrogen-bonded nitriles. The chemical exchange rate depends upon voltage, with the hydrogen bond of the water to the nitriles breaking 2 to 3 times slower (>63 vs. 25 ps) under a positive as compared to a negative potential. Spectral diffusion created by hydrogen-bond fluctuations occurs on a ~1 ps timescale and is moderately potential-dependent. Timescales from molecular dynamics simulations agree qualitatively with the experiment and show that a negative voltage causes a small net displacement of water away from the surface. These results show that the voltage applied to an electrode can alter the timescales of solvent motion at its interface, which has implications for electrochemically driven reactions.

Switching of Redox Levels Leads to High Reductive Stability in Water-in-Salt Electrolytes

Developing nonflammable electrolytes with wide electrochemical windows is of great importance for energy storage devices. Water-in-salt electrolytes (WiSE) have attracted great interests due to their widely opened electrochemical windows and high stability. Previous theoretical investigations have revealed changes in solvation shell of water molecules result in opening of HOMO-LUMO gaps of water, leading to the formation of an anion-derived solid-electrolyte-interphase (SEI) in WiSE. However, how solvation structures affect electrochemical windows at atomic level is still a puzzle, which hinders optimization and design of aqueous electrolytes. Herein, machine learning molecular dynamics and free energy calculation method are applied to compute redox potentials of anions of Li-salts and water of aqueous electrolytes at a range of salt concentrations. Furthermore, an analysis based on local solvation structures is employed to demonstrate the structure-property relations. Our calculation shows that the hydrogen evolution reaction is impeded in WiSE due to switching of the order of redox levels of the anion and H2O, leading to formation of SEI and high reductive stability. Level switching is caused by the special solvation environments of isolated water molecules. Our work provides new insight into the electrochemistry of aqueous electrolytes which would benefit the electrolyte design in energy storage devices.

Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning

Developing electrolytes with excellent electrochemical stability is critical for next-generation rechargeable batteries. Super-concentrated electrolytes (SCEs) have attracted great interest due to their high electrochemical performances and stability. Previous studies have revealed changes in solvation structures and shifts in lowest unoccupied molecular orbitals from solvents to anions, promoting the formation of an anion-derived solid-electrolyte-interphase (SEI) in SCE. However, a direct connection at the atomic level to electrochemical properties is still missing, hindering the rational optimization of electrolytes. Herein, we combine ab initio molecular dynamics with the free energy calculation method to compute redox potentials of propylene carbonate electrolytes at a range of LiTFSI concentrations, and moreover employ an unsupervised machine learning model with a local structure descriptor to establish the structure-property relations. Our calculation indicates that the network of TFSI- in SCE not only helps stabilize the added electron and renders the anion more prone to reductive decomposition, but also impedes the solvation of F- and favors LiF precipitation, together leading to effective formation of protective SEI layers. Our work provides new insights into the solvation structures and electrochemistry of concentrated electrolytes which are essential to electrolyte design in batteries.

Electron Transfer of Functionalised Quinones in Acetonitrile

Quinones are redox active organic molecules that have been proposed as an alternative choice to metal-based materials in electrochemical energy storage devices. Functionalization allows to fine tune not only their chemical stability but also the redox potential and the kinetics of the electron transfer reaction. However, reaction rate constant is not solely determined by the redox species but is also impacted by solvent effects. In this work, we show how the functionalization of benzoquinone with different functional groups impacts the solvent reorganization free energies of electron transfer half-reactions in acetonitrile. The use of molecular density functional theory, whose computational cost for studying electron transfer reaction is considerably reduced compared to state-of-the art molecular dynamics simulations, enables to perform a systematic study. We validate the method by comparing the predictions of the solvation shell structure and the free energy profiles for electron transfer reaction to reference classical molecular dynamics simulations in the case of anthraquinone solvated in acetonitrile. We show that all the studied electron transfer half-reactions follow Marcus' description, regardless of functional groups. Consequently, the solvent reorganization free energy decreases as the molecular size increases.

Automated Workflow for Computation of Redox Potentials, Acidity Constants and Solvation Free Energies Accelerated by Machine Learning

Fast evolution of modern society stimulates intense development of new materials with novel functionalities in energy and environmental applications. Due to rapid progress of computer science, computational design of materials with target properties has recently attracted lots of interests. Accurate and efficient calculation of fundamental thermodynamic properties, including redox potentials, acidity constants, and solvation free energies, is of great importance for selection and design of desirable materials. Free energy calculation based on ab initio molecular dynamics (AIMD) can predict these properties with high accuracy at complex environments, however being impeded by high computational costs. To address this issue, this work develops an automated scheme that combines iterative training of machine learning potentials (MLPs) and free energy calculation, and deomonstrates that these thermodynamic properties can be computed by ML accelerated MD with ab initio accuracy and much longer time scale at cheaper costs, improving poor statistics and convergence of numerical integration by AIMD. Our automated scheme lays the foundation for computational chemistry-assisted materials design.

The energetics of electron and proton transfer to CO2 in aqueous solution

The electrocatalytic reduction of CO₂ is considered an effective method to reduce CO₂ emissions and achieve electrical/chemical energy conversion. It is crucial to determine the reaction mechanism so that the key reaction intermediates can be targeted and the overpotential lowered. The process involves the interaction with the electrode surface and with species, including the solvent, at the electrode-electrolyte interface, and it is therefore not easy to separate catalytic contributions of the electrode from those of the electrolyte. We have used density functional theory-based molecular dynamics to calculate the Gibbs free energy of the proton and electron transfer reactions corresponding to each step in the electroreduction of CO₂ to HCOOH in aqueous media. The results show thermodynamic pathways consistent with the mechanism proposed by Hori. Since electrodes are not included in this work, differences between the calculated results and the experimental observations can help determine the catalytic contribution of the electrode surface.