Bridging Scales and Paradigms: A Perspective from Atomistic Simulation to AI-Enhanced Modeling of Sodium-Ion Capacitors

Abstract

Sodium-ion supercapacitors are emerging as a promising alternative for large-scale energy storage due to the natural abundance and low cost of sodium. However, their practical performance is constrained by a limited understanding of the underlying mechanisms governing ion storage, electrolyte solvation, and electrode-electrolyte interfacial behavior. In this Perspective, we provide a comprehensive overview of the simulation techniques employed to unravel the multiscale physics of SICs, including classical molecular dynamics, density functional theory, ab initio molecular dynamics, quantum mechanics/molecular mechanics hybrids, and emerging machine learning force fields. We highlight recent modeling advances in electrode material design (e.g., nanoporous carbons, MOFs, MXenes), electrolyte optimization (e.g., solvation structure, ion transport mechanisms), and interface engineering (e.g., electric double layer structure, capacitance, dielectric response). Furthermore, we identify key challenges in current simulation paradigms, such as scale-accuracy trade-offs, insufficient sampling, and limited transferability under extreme conditions. To address these issues, we propose future directions that integrate physics-informed machine learning, multiscale modeling, and large language model-driven knowledge discovery to enable rational, closed-loop design of high-performance sodium-ion supercapacitors. This work aims to catalyze a paradigm shift from empirical trial-and-error to data-driven, AI-assisted material and device engineering in sodium-ion energy storage systems.