ArticleNature communications2026
Design topological materials by reinforcement fine-tuned generative model.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Topological insulators and topological crystalline insulators are characterized by robust surface states and insulating bulk behavior, rendering them highly valuable for quantum computing, spintronics, and other emerging technologies. However, the discovery of such materials-particularly those with a full band gap-remains challenging, primarily due to the limitations of conventional approaches that rely on the screening of known materials. Here, we employ reinforcement fine-tuning on a pre-trained generative model to facilitate the discovery of topological materials. This approach enables targeted material generation while preserving chemical validity and structural stability. Our fine-tuned model significantly improves the likelihood of generating topological insulators and topological crystalline insulators, leading to the identification of numerous new candidate materials. Among them, Ge
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