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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2509.03081 |
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| _version_ | 1866911136620740608 |
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| author | Li, Xiaoying Tu, Wenqian Lv, Run Liu, Li'e Shao, Dingfu Sun, Yuping Lu, Wenjian |
| author_facet | Li, Xiaoying Tu, Wenqian Lv, Run Liu, Li'e Shao, Dingfu Sun, Yuping Lu, Wenjian |
| contents | Superconductor research has traditionally depended on experiments and theoretical approaches. However, the rapid advancement of data-driven methods and machine learning (ML) has opened avenues for accelerating superconductor discovery. Here, we integrated ML with density functional theory (DFT) calculations to efficiently screen conventional B-C-N based superconductors and identify potential high-TC candidates among R3Ni2O7-type bilayer nickelates. We identified 12 new binary and ternary B-C-N based superconductors with TC >= 10 K, including 3 with TC >= 25 K, such as two structural forms of B2CN (TC = 44.8 K and 41.5 K) and TiNbN2 (TC = 26.2 K). These materials share a common feature of strong σ-bonds, which is key to achieving relatively high TC. Moreover, we proposed Tb3Ni2O7 (TC = 61.6 K) and Ac3Ni2O7 (TC = 70.3 K) as potential high-TC nickelate superconductors under high pressure. Their electronic structures closely resemble those of La3Ni2O7, especially in the hole-type band dominated by Ni-3dz2 orbital character. We also analyzed feature importance in the ML results for both conventional and high-TC superconductors. These results advance the search for new superconductors and enhance the fundamental understanding of superconducting mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning-accelerated search of superconductors in B-C-N based compounds and R3Ni2O7-type nickelates Li, Xiaoying Tu, Wenqian Lv, Run Liu, Li'e Shao, Dingfu Sun, Yuping Lu, Wenjian Superconductivity Materials Science Superconductor research has traditionally depended on experiments and theoretical approaches. However, the rapid advancement of data-driven methods and machine learning (ML) has opened avenues for accelerating superconductor discovery. Here, we integrated ML with density functional theory (DFT) calculations to efficiently screen conventional B-C-N based superconductors and identify potential high-TC candidates among R3Ni2O7-type bilayer nickelates. We identified 12 new binary and ternary B-C-N based superconductors with TC >= 10 K, including 3 with TC >= 25 K, such as two structural forms of B2CN (TC = 44.8 K and 41.5 K) and TiNbN2 (TC = 26.2 K). These materials share a common feature of strong σ-bonds, which is key to achieving relatively high TC. Moreover, we proposed Tb3Ni2O7 (TC = 61.6 K) and Ac3Ni2O7 (TC = 70.3 K) as potential high-TC nickelate superconductors under high pressure. Their electronic structures closely resemble those of La3Ni2O7, especially in the hole-type band dominated by Ni-3dz2 orbital character. We also analyzed feature importance in the ML results for both conventional and high-TC superconductors. These results advance the search for new superconductors and enhance the fundamental understanding of superconducting mechanisms. |
| title | Machine learning-accelerated search of superconductors in B-C-N based compounds and R3Ni2O7-type nickelates |
| topic | Superconductivity Materials Science |
| url | https://arxiv.org/abs/2509.03081 |