The search for superionic solid-state electrolytes using a physics-informed generative model

Abstract

The discovery of superionic solid-state electrolytes for cation batteries is currently limited by the range of materials available in online materials databases. Generative artificial intelligence approaches have recently been applied to overcome this limitation and explore unknown stoichiometries and structures, but efficiently generating candidates that satisfy strict stability criteria remains challenging. Here we introduce a physics-informed hierarchical generative framework that leverages symmetry-aware crystallographic principles to systematically explore molecular configurations, lattice parameters, and bonding environments. Our approach integrates empirical physical constraints and reinforcement learning utilizing a hierarchical state representation to generate chemically valid and structurally stable candidates. We propose symmetry-aware hierarchical architecture for flow-based traversal with density (SHAFT-density) that ensures efficient exploration of the material search space, prioritizing low formation energy, molecular packing optimized for stability and conductivity, and enhanced electrochemical properties. We discovered new binary and ternary metastable phases, of which we find highly conductive LiBr, LiCl, Li₂IBr, and Li₃CBr₂. These materials can either function as solid-state electrolyte materials or be part of solid-state electrolyte mixtures. Our results demonstrate the model's capability to identify stable, diverse, and potentially superionic compounds, offering promising candidates for developing next-generation solid-state electrolytes with improved characteristics.