Electrolyte hydration energy as a universal descriptor for ion-specific capacitance: insights from interpretable machine learning

The rational design of high-performance electrochemical energy storage devices critically depends on a fundamental understanding of ion-electrode interactions at the molecular scale. Herein, we employ interpretable machine learning (ML) to reveal electrolyte hydration energy as a universal descriptor governing ion-specific capacitance in two-dimensional (2D) materials. Through explainable ML, we elucidate how ion hydration shell stability and size critically influence charge transport and storage at the electrode-electrolyte interface. Our analysis identifies hydration energy – not ionic size – as the primary factor dictating capacitance, challenging prevailing assumptions and providing quantifiable design rules for electrolyte selection. These insights offer a data-driven pathway to optimize 2D materials for supercapacitors and beyond, including batteries and electrocatalytic systems. This work demonstrates the power of explainable artificial intelligence in uncovering molecular-level mechanisms that accelerate the discovery and development of next-generation energy storage technologies.