The electrochemical production of hydrogen or synthetic fuels forms one of the main pillars of future sustainable energy storage. However, the electrode materials used in current electrolyzers do not yet achieve these chemical conversion processes efficiently enough or corrode too quickly. The search for suitable more active and/or durable materials is therefore a highly active research field. The use of modern computer simulations could complement lengthy and complex experiments, thus contributing to the urgently needed shortening of long research and development cycles.
However, computer simulations can only fulfill this function if they reliably describe real systems. To accurately capture the chemical conversions, this description must go down to the details of the atomic structure, and unfortunately, even after years of intensive research, there are still unresolved problems. A long-known issue is that previous atomically-resolved simulations could not correctly reproduce the experimental capacity of even a relatively simple but prototypical model electrode. The capacity calculated for this defined single-crystal surface of platinum, i.e., the intrinsic storage capacity, always came out at least a factor of 10 too small.
Researchers in the Theory Department of the Fritz Haber Institute have now traced this problem back to the classical nature of the simulation techniques used so far. Lang Li, the first author of the study published in the renowned Journal of the American Society, explains: "By classical, we mean that quantum mechanical effects have not been explicitly considered in the simulations so far." In complex simulations that include these effects, she and the team led by Dr. Nicolas Hörmann were able to fully confirm the experimental values. Specifically, their analyses showed that electrons penetrate from the surface of the platinum electrode into the first layers of water in the surrounding electrolyte to some extent, and it is this expansion that significantly increases the capacity.
With this knowledge, future computer simulations for promising new electrode materials can now be specifically improved. One approach could be machine learning methods that, after suitable training on complex quantum mechanical data, effectively incorporate this so-called electron spillover into more efficient classical simulations.
Journal of the American Chemical Society
Electron Spillover into Water Layers: A Quantum Leap in Understanding Capacitance Behavior
18-Jun-2025