Discovery of new topological insulators and semimetals using deep generative models
Abstract Topological materials possess unique electronic properties and hold immense attraction to both fundamental physics research and practical applications. Over the past decades, the discovery of new topological materials has relied on the symmetry-based analysis of the quantum wave function. I...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Quantum Materials |
| Online Access: | https://doi.org/10.1038/s41535-025-00731-0 |
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| _version_ | 1832572014000340992 |
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| author | Tao Hong Taikang Chen Dalong Jin Yu Zhu Heng Gao Kun Zhao Tongyi Zhang Wei Ren Guixin Cao |
| author_facet | Tao Hong Taikang Chen Dalong Jin Yu Zhu Heng Gao Kun Zhao Tongyi Zhang Wei Ren Guixin Cao |
| author_sort | Tao Hong |
| collection | DOAJ |
| description | Abstract Topological materials possess unique electronic properties and hold immense attraction to both fundamental physics research and practical applications. Over the past decades, the discovery of new topological materials has relied on the symmetry-based analysis of the quantum wave function. In this study, we propose an efficient inverse design method CTMT (CTMT: CDVAE, Topogivity, interatomic potentials (IAPs) as realized in M3GNet, and TQC) utilizing deep generative machine learning models to discover novel topological insulators and semimetals in a much-fast and low-cost manner. This method covers the entire process of new crystal structure generation, heuristic rule screening, fast stability estimation, and topology type diagnosis, resulting in 4 topological insulators and 16 topological semimetals. Especially, the newly discovered topological materials include several chiral Kramers-Weyl fermion semimetals and chiral materials with low symmetry, whose topology is previously considered challenging to discern. These findings demonstrate the capability of CTMT in discovering topological materials and its great potential for data-driven inverse design of advanced functional materials. |
| format | Article |
| id | doaj-art-87e54986efac4336aacbe6b4322f5895 |
| institution | Kabale University |
| issn | 2397-4648 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Materials |
| spelling | doaj-art-87e54986efac4336aacbe6b4322f58952025-02-02T12:07:19ZengNature Portfolionpj Quantum Materials2397-46482025-01-011011810.1038/s41535-025-00731-0Discovery of new topological insulators and semimetals using deep generative modelsTao Hong0Taikang Chen1Dalong Jin2Yu Zhu3Heng Gao4Kun Zhao5Tongyi Zhang6Wei Ren7Guixin Cao8Materials Genome Institute, Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials, Shanghai UniversityDepartment of Physics, Shanghai Key Laboratory of High Temperature Superconductors, International Centre of Quantum and Molecular Structures, Shanghai UniversityDepartment of Physics, Shanghai Key Laboratory of High Temperature Superconductors, International Centre of Quantum and Molecular Structures, Shanghai UniversityDepartment of Physics, Shanghai Key Laboratory of High Temperature Superconductors, International Centre of Quantum and Molecular Structures, Shanghai UniversityDepartment of Physics, Shanghai Key Laboratory of High Temperature Superconductors, International Centre of Quantum and Molecular Structures, Shanghai UniversitySchool of Physics, Nanjing University of Science and TechnologyMaterials Genome Institute, Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials, Shanghai UniversityDepartment of Physics, Shanghai Key Laboratory of High Temperature Superconductors, International Centre of Quantum and Molecular Structures, Shanghai UniversityMaterials Genome Institute, Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials, Shanghai UniversityAbstract Topological materials possess unique electronic properties and hold immense attraction to both fundamental physics research and practical applications. Over the past decades, the discovery of new topological materials has relied on the symmetry-based analysis of the quantum wave function. In this study, we propose an efficient inverse design method CTMT (CTMT: CDVAE, Topogivity, interatomic potentials (IAPs) as realized in M3GNet, and TQC) utilizing deep generative machine learning models to discover novel topological insulators and semimetals in a much-fast and low-cost manner. This method covers the entire process of new crystal structure generation, heuristic rule screening, fast stability estimation, and topology type diagnosis, resulting in 4 topological insulators and 16 topological semimetals. Especially, the newly discovered topological materials include several chiral Kramers-Weyl fermion semimetals and chiral materials with low symmetry, whose topology is previously considered challenging to discern. These findings demonstrate the capability of CTMT in discovering topological materials and its great potential for data-driven inverse design of advanced functional materials.https://doi.org/10.1038/s41535-025-00731-0 |
| spellingShingle | Tao Hong Taikang Chen Dalong Jin Yu Zhu Heng Gao Kun Zhao Tongyi Zhang Wei Ren Guixin Cao Discovery of new topological insulators and semimetals using deep generative models npj Quantum Materials |
| title | Discovery of new topological insulators and semimetals using deep generative models |
| title_full | Discovery of new topological insulators and semimetals using deep generative models |
| title_fullStr | Discovery of new topological insulators and semimetals using deep generative models |
| title_full_unstemmed | Discovery of new topological insulators and semimetals using deep generative models |
| title_short | Discovery of new topological insulators and semimetals using deep generative models |
| title_sort | discovery of new topological insulators and semimetals using deep generative models |
| url | https://doi.org/10.1038/s41535-025-00731-0 |
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