Jiang Jiajun, Xue Yamin, Xiong Zehui, Yao Shunwei, Cheng Zebang, Hu Wenjing, Zhang Duoduo, Zhang Guoliang, Zhu Renjie, Zha Liliang, Wang Ziqiu, Peng Lin, Shi Tingting, Zhang Yufeng, Chen Jing, Liu Xiaolin, Lin Jia
Department of Physics, Shanghai University of Electric Power, Shanghai 200090, China.
School of Physics, Sun Yat-Sen University, Guangzhou 510275, China.
J Chem Phys. 2025 Jul 7;163(1). doi: 10.1063/5.0268667.
In the field of hydride superconductors, a great challenge is to achieve superconducting states under ambient pressure conditions rather than the extreme high-pressure environments that have been required in experiments. Achieving this goal is crucial for advancing the practical applications of high-temperature superconducting materials. We discover a family of compounds (hydride double perovskite superconductors with space group Fm3̄m and chemical formula A2MM'H6) to achieves this goal. A machine-learning-accelerated approach is utilized to search for hydride double perovskite superconductors under ambient pressure within an extensive dataset comprising over 106 535 hypothetical compounds. 15 stable hydride double perovskite superconductors are discovered under ambient pressure, with the highest superconducting transition temperature (Tc) reaching 18.7 K. The structural stability, electronic properties, and superconducting behavior of these materials have been comprehensively analyzed. Phonon dispersion analysis has highlighted the critical role of lattice vibrations in electron-phonon coupling (EPC), where the contribution of H atom vibrations is essential for facilitating electron pairing and the onset of superconductivity. This demonstrates that the machine-learning-accelerated approach is a highly effective method and can be easily extended to other compounds.
在氢化物超导体领域，一个巨大的挑战是在环境压力条件下实现超导态，而非实验中所需的极端高压环境。实现这一目标对于推进高温超导材料的实际应用至关重要。我们发现了一类化合物（空间群为Fm3̄m且化学式为A2MM'H6的氢化物双钙钛矿超导体）来实现这一目标。一种机器学习加速方法被用于在包含超过106535种假设化合物的广泛数据集中搜索环境压力下的氢化物双钙钛矿超导体。在环境压力下发现了15种稳定的氢化物双钙钛矿超导体，其最高超导转变温度（Tc）达到18.7 K。这些材料的结构稳定性、电子性质和超导行为已得到全面分析。声子色散分析突出了晶格振动在电子 - 声子耦合（EPC）中的关键作用，其中H原子振动的贡献对于促进电子配对和超导的起始至关重要。这表明机器学习加速方法是一种非常有效的方法，并且可以很容易地扩展到其他化合物。
