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

TL;DR

TXL Fusion is a hybrid machine learning framework that combines chemical heuristics, physical descriptors, and large language models to efficiently discover topological materials, validated by DFT calculations.

Contribution

It introduces a novel hybrid approach integrating chemical heuristics and LLM embeddings for topological materials discovery, improving accuracy and interpretability.

Findings

Successfully identified new topological material candidates.

Validated predictions with density functional theory (DFT).

Enhanced classification accuracy over traditional methods.

Abstract

Topological materials--including insulators (TIs) and semimetals (TSMs)--hold immense promise for quantum technologies, yet their discovery remains constrained by the high computational cost of first-principles calculations and the slow, resource-intensive nature of experimental synthesis. Here, we introduce TXL Fusion, a hybrid machine learning framework that integrates chemical heuristics, engineered physical descriptors, and large language model (LLM) embeddings to accelerate the discovery of topological materials. By incorporating features such as space group symmetry, valence electron configurations, and composition-derived metrics, TXL Fusion classifies materials across trivial, TSM, and TI categories with improved accuracy and generalization compared to conventional approaches. The framework successfully identified new candidates, with representative cases further validated through density functional theory (DFT), confirming its predictive robustness. By uniting data-driven learning with chemical intuition, TXL Fusion enables rapid and interpretable exploration of complex materials spaces, establishing a scalable paradigm for the intelligent discovery of next-generation topological and quantum materials.