用强化学习微调生成模型，高效发现新型拓扑绝缘体。
Design Topological Materials by Reinforcement Fine-Tuned Generative Model
- 用强化学习微调预训练生成模型，使其聚焦于生成拓扑材料。
- 成功生成大量新拓扑材料，其中Ge₂Bi₂O₆带隙达0.26 eV，为已知最大之一。
- 适合材料设计与人工智能结合的研究者参考。
拓扑绝缘体（TIs）和拓扑晶体绝缘体（TCIs）具有非平凡的电子特性，其发现对实际应用极具价值。然而，特别是具有全带隙的材料仍十分稀少。传统方法受限于已知材料库的扫描，难以突破。本文提出通过生成模型主动设计新拓扑材料，具体采用强化学习微调（ReFT）优化预训练生成模型，使其目标与材料设计需求对齐。实验表明，该方法显著提升生成TI与TCI的能力，且生成材料稳定性保持良好。利用微调后的模型，成功发现大量新拓扑材料，其中代表性材料Ge₂Bi₂O₆为全带隙拓扑绝缘体，带隙达0.26 eV，位居已知同类材料前列。
原文摘要 · Abstract (English)
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.
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