TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery

Abstract

Topological materials, including topological insulators (TIs) and topological semimetals (TSMs), offer promising platforms for quantum, spintronic, and low-dissipation electronic technologies. Their discovery, however, remains constrained by the high cost of first-principles calculations and the slow, resource-intensive nature of experimental validation. Here, we introduce TXL Fusion, a hybrid machine-learning framework that integrates chemically inspired heuristics, physically interpretable numerical descriptors, and large language model (LLM)-derived semantic embeddings for topological-materials classification and discovery. By combining space-group symmetry, electron-count and orbital descriptors, composition-derived topological heuristics, and physics-aware semantic representations, TXL Fusion classifies materials into trivial, TSM, and TI categories with improved overall performance and enhanced minority-class TI recognition relative to conventional descriptor-based baselines. The model further serves as a high-throughput pre-screening tool for external discovery spaces, rapidly prioritizing candidate TSMs before expensive first-principles or experimental validation. Representative TXL-prioritized candidates were subsequently supported by density functional theory (DFT) calculations, demonstrating the practical value of the framework for reducing discovery cost. By uniting symbolic chemical rules, statistical learning, and language-based representations, TXL Fusion provides a scalable and interpretable strategy for accelerating the discovery of next-generation topological and quantum materials.