Graph Symbolic Distillation: Decoding Graph Data into Interpretable Formulas across Materials and Chemistry

Abstract

Graph data provide a natural representation of the interconnected structures and interactions underlying many chemical and materials systems. Graph neural networks (GNNs) can learn complex relationships from such data, yet translating these representations into interpretable descriptors and scientific insights remains challenging. Here we introduce graph symbolic distillation (GSD), a framework for decoding graph data into interpretable symbolic formulas. GSD identifies task-relevant message-passing architectures and distills their learned transformations while preserving graph connectivity and aggregation operations. Across three case studies spanning bulk materials, molecules, and surfaces, GSD not only recovers expert-defined descriptors from elementary node and edge features, but also reveals relationships emerging from their organization in complex graphs: extremal local geometric descriptors for band gap variations in $γ$-CsPbI$_3$, a local polarity--size relationship underlying hydration free energy, and site-specific electronic--connectivity coupling in multidentate adsorption. GSD thus provides a general route from graph data to interpretable descriptors and new, testable scientific insights.