Design Topological Materials by Reinforcement Fine-Tuned Generative Model
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916029526966272 |
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| author | Xu, Haosheng Qian, Dongheng Liu, Zhixuan Jiang, Yadong Wang, Jing |
| author_facet | Xu, Haosheng Qian, Dongheng Liu, Zhixuan Jiang, Yadong Wang, Jing |
| contents | Topological insulators (TIs) and topological crystalline insulators (TCIs) are materials with unconventional electronic properties, making their discovery highly valuable for practical applications. However, such materials, particularly those with a full band gap, remain scarce. Given the limitations of traditional approaches that scan known materials for candidates, we focus on the generation of new topological materials through a generative model. Specifically, we apply reinforcement fine-tuning (ReFT) to a pre-trained generative model, thereby aligning the model's objectives with our material design goals. We demonstrate that ReFT is effective in enhancing the model's ability to generate TIs and TCIs, with minimal compromise on the stability of the generated materials. Using the fine-tuned model, we successfully identify a large number of new topological materials, with Ge$_2$Bi$_2$O$_6$ serving as a representative example--a TI with a full band gap of 0.26 eV, ranking among the largest known in this category. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design Topological Materials by Reinforcement Fine-Tuned Generative Model Xu, Haosheng Qian, Dongheng Liu, Zhixuan Jiang, Yadong Wang, Jing Materials Science Artificial Intelligence Topological insulators (TIs) and topological crystalline insulators (TCIs) are materials with unconventional electronic properties, making their discovery highly valuable for practical applications. However, such materials, particularly those with a full band gap, remain scarce. Given the limitations of traditional approaches that scan known materials for candidates, we focus on the generation of new topological materials through a generative model. Specifically, we apply reinforcement fine-tuning (ReFT) to a pre-trained generative model, thereby aligning the model's objectives with our material design goals. We demonstrate that ReFT is effective in enhancing the model's ability to generate TIs and TCIs, with minimal compromise on the stability of the generated materials. Using the fine-tuned model, we successfully identify a large number of new topological materials, with Ge$_2$Bi$_2$O$_6$ serving as a representative example--a TI with a full band gap of 0.26 eV, ranking among the largest known in this category. |
| title | Design Topological Materials by Reinforcement Fine-Tuned Generative Model |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13048 |