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Design topological materials by reinforcement fine-tuned generative model.

Haosheng Xu1,2, Dongheng Qian1,2, Zhixuan Liu1,2

  • 1State Key Laboratory of Surface Physics and Department of Physics, Fudan University, Shanghai, China.

Nature Communications
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new AI method for discovering advanced topological materials, like topological insulators. This approach accelerates the search for materials crucial for quantum computing and spintronics.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Artificial Intelligence

Background:

  • Topological insulators and topological crystalline insulators possess unique electronic properties, such as robust surface states and insulating bulk.
  • These materials are critical for next-generation technologies, including quantum computing and spintronics.
  • Discovering new topological materials, especially those with wide band gaps, is hindered by traditional screening methods.

Purpose of the Study:

  • To develop and apply a novel AI-driven approach for the targeted discovery of topological materials.
  • To overcome the limitations of conventional material screening methods.
  • To identify new candidate topological insulators and topological crystalline insulators with desirable properties.

Main Methods:

  • Utilized reinforcement fine-tuning on a pre-trained generative model.
  • Employed targeted material generation ensuring chemical validity and structural stability.
  • Screened generated materials for topological properties and band gaps.

Main Results:

  • The fine-tuned generative model significantly increased the probability of generating topological insulators and topological crystalline insulators.
  • Identified numerous novel candidate topological materials.
  • Discovered Ge2Bi2O6 as a strong topological insulator with a substantial band gap of 0.26 eV.

Conclusions:

  • Reinforcement-based generative design is a powerful strategy for discovering materials with specific topological properties.
  • The developed AI approach accelerates the identification of promising topological materials.
  • Ge2Bi2O6 represents a significant advancement in the search for practical topological materials.