Decoding the chemical space of fast-ion conductors via a descriptor-guided transfer learning framework

Abstract

Fast-ion conductors (FICs) are key components for next-generation high-performance batteries, yet predicting ion mobility remains challenging because of the unclear transport mechanisms. This difficulty is further compounded by experimental datasets that lack precise crystal structures. Here, we present a descriptor-guided transfer learning framework, named IonNet, to predict ion mobility for compounds, regardless of structure accessibility. IonNet adopts a multichannel subnetwork architecture that captures universal representations by integrating static and statistical descriptors of compounds. We demonstrate the exceptional performance of IonNet in predicting ion mobility, consistently outperforming 16 ablation study combinations and previous deep learning models. Leveraging the universal adaptability of chemical representations, IonNet not only uncovers 87 FICs among ∼4500 stable perfectly stoichiometric compounds but also efficiently pinpoints ∼63,000 prospective FICs from ∼5 million substituted compounds. This study not only presents a full-chain artificial intelligence tool for identifying FICs but also offers compositional principles governing ion mobility, thereby accelerating the development of energy storage and conversion.