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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.04068 |
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| _version_ | 1866912927347376128 |
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| author | Ullah, Arif Islam, Rajibul Hussain, Ghulam Muhammad, Zahir Li, Xiaoguang Yang, Ming |
| author_facet | Ullah, Arif Islam, Rajibul Hussain, Ghulam Muhammad, Zahir Li, Xiaoguang Yang, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04068 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery Ullah, Arif Islam, Rajibul Hussain, Ghulam Muhammad, Zahir Li, Xiaoguang Yang, Ming Materials Science 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. |
| title | TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery |
| topic | Materials Science |
| url | https://arxiv.org/abs/2511.04068 |