Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Xiaoyang Zhang, Chengqian Wang, Zhenyu Liu, Hanyu Lv, Jian Wang, Han E, Weinan Ma, Yanming |
| author_facet | Wang, Xiaoyang Zhang, Chengqian Wang, Zhenyu Liu, Hanyu Lv, Jian Wang, Han E, Weinan Ma, Yanming |
| contents | The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant bottleneck for both experimental and theoretical investigations. In this study, we employ the design of high-temperature ternary superhydride superconductors as a representative case to demonstrate how this challenge can be well addressed through a deep-learning-driven theoretical framework. This framework integrates high-throughput crystal structure exploration, physics-informed screening, and accurate prediction of superconducting critical temperatures. Our approach enabled the exploration of approximately 36 million ternary hydride structures across a chemical space of 29 elements, leading to the identification of 144 potential high-Tc superconductors with predicted Tc > 200 K and superior thermodynamic stability at 200 GPa. Among these, 129 compounds spanning 27 novel structural prototypes are reported for the first time, representing a significant expansion of the known structural landscape for hydride superconductors. This work not only greatly expands the known repertoire of high-Tc hydride superconductors but also establishes a scalable and efficient methodology for navigating the complex landscape of multinary hydrides. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning Wang, Xiaoyang Zhang, Chengqian Wang, Zhenyu Liu, Hanyu Lv, Jian Wang, Han E, Weinan Ma, Yanming Superconductivity Computational Physics The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant bottleneck for both experimental and theoretical investigations. In this study, we employ the design of high-temperature ternary superhydride superconductors as a representative case to demonstrate how this challenge can be well addressed through a deep-learning-driven theoretical framework. This framework integrates high-throughput crystal structure exploration, physics-informed screening, and accurate prediction of superconducting critical temperatures. Our approach enabled the exploration of approximately 36 million ternary hydride structures across a chemical space of 29 elements, leading to the identification of 144 potential high-Tc superconductors with predicted Tc > 200 K and superior thermodynamic stability at 200 GPa. Among these, 129 compounds spanning 27 novel structural prototypes are reported for the first time, representing a significant expansion of the known structural landscape for hydride superconductors. This work not only greatly expands the known repertoire of high-Tc hydride superconductors but also establishes a scalable and efficient methodology for navigating the complex landscape of multinary hydrides. |
| title | Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning |
| topic | Superconductivity Computational Physics |
| url | https://arxiv.org/abs/2502.16558 |