Grotthuss transfer is responsible for a large increase in the self-diffusion of hydroxide and hydronium ions in aqueous solutions compared to similarly sized ions. Recent advances in machine-learning molecular dynamics have shown some success in capturing this process. In the present work, we show that classical molecular dynamics combined with experimentally measured electrical conductivities can also be used to determine self-diffusion coefficients and the lifetimes of hydroxide and hydronium ions in aqueous KOH, NaOH, and HCl solutions. This was tested and validated across a wide range of concentrations at 25 and 60 °C. The approach relies on augmenting classically computed trajectories with a biased random walk, which together accounts for both vehicular transport and Grotthuss transfer. The concentration and temperature dependence of this random walk are calibrated by comparing simulated electrical conductivities with available experimental electrical conductivity data. The computed self-diffusion coefficients match measurements at infinite dilution and results from machine learning molecular dynamics. Ion lifetimes reported by machine learning and ab initio molecular dynamics studies depend strongly on the precise definition of what constitutes a Grotthuss transfer event. Our approach for calculating ion lifetimes does not have this drawback. We also show that our self-diffusion coefficients and electrical conductivities are insensitive to the precise definition of what constitutes a Grotthuss transfer event.

This extensive review highlights the central role of classical molecular simulation in advancing hydrogen (H2) technologies. As the transition to a sustainable energy landscape is urgently needed, the optimization of H2 processes, spanning production, purification, transportation, storage, safety, and utilization is essential. To this end, accurate prediction of thermodynamic, transport, structural, and interfacial properties is important for overcoming engineering challenges across the entire H2 value chain. Experimental measurements, despite being the traditional way of obtaining these properties, can be limited by the distinctive nature of H2, harsh operating conditions, safety constraints, and extensive parameter spaces. Free from such limitations, classical molecular simulations, in the general frameworks of Monte Carlo and Molecular Dynamics, provide an optimal balance between computational efficiency and accuracy, bridging the gap between quantum mechanical calculations and macro-scale modeling. This review also systematically covers molecular simulation methods and force fields for computing key properties of H2 systems, such as phase and adsorption equilibria and transport coefficients. Beyond property prediction, we explore how molecular simulation reveals fundamental mechanisms governing hydrate formation and dissociation, membrane permeations, and H2 embrittlement. When possible, data from multiple sources are compared and critically assessed, while effort is put on evaluating the force fields used and methodological approaches followed in the literature. Finally, this review aims at identifying research gaps and future opportunities, emphasizing emerging approaches, such as molecular simulation in the era of artificial intelligence.

Accurate conductivity predictions of KOH(aq) are crucial for electrolysis applications. OH– is transferred in water by the Grotthuss transfer mechanism, thereby increasing its mobility compared to that of other ions. Classical and ab initio molecular dynamics struggle to capture this enhanced mobility due to limitations in computational costs or in capturing chemical reactions. Most studies to date have provided only qualitative descriptions of the structure during Grotthuss transfer, without quantitative results for the transfer rate and the resulting transport properties. Here, machine learning molecular dynamics is used to investigate 50,000 transfer events. Analysis confirmed earlier works that Grotthuss transfer requires a reduction in accepted and a slight increase in donated hydrogen bonds to the hydroxide, indicating that hydrogen-bond rearrangements are rate-limiting. The computed self-diffusion coefficients and electrical conductivities are consistent with experiments for a wide temperature range, outperforming classical interatomic force fields and earlier AIMD simulations.