通过机器学习加速在环境压力下对化物双矿超导体材料空间的探索
Jiajun Jiang1, Yamin Xue1, Zehui Xiong1
1Department of Physics, Shanghai University of Electric Power, Shanghai 200090, China.
The Journal of chemical physics
|July 2, 2025
概括
研究人员使用机器学习发现了新的环境压力化物超导体. 这一突破推动了对高温超导材料的实际应用,通过确定了15种具有更广泛使用潜力的稳定化合物.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算化学计算化学
背景情况:
- 在环境压力下实现超导是材料科学中的一个主要挑战.
- 当前的化物超导体通常需要极端高压条件,限制了实际应用.
- 开发环境压力超导体是推进高温超导技术的关键.
研究的目的:
- 发现新的化物超导体,在环境压力下表现出稳定的超导状态.
- 利用机器学习加速方法,有效地探索潜在的超导材料.
- 分析新发现的环境压力化物超导体的特性和机制.
主要方法:
- 采用机器学习加速计算方法来选一个包含超过106,535个假设化合物的大型数据集.
- 搜索的重点是化物双矿,其空间组Fm3̄m和化学公式A2MM'H6.
- 对结构稳定性,电子性质和超导行为进行了全面的分析,包括语音分散和电子-语音合.
主要成果:
- 确定了一个由15个稳定化物双矿超导体组成的家族,在环境压力下运行.
- 发现的最高超导过渡温度 (Tc) 达到18.7 K.
- 声波分散分析证实了网格振动,特别是H原子振动在电子-声波合和超导性中的关键作用.
结论:
- 机器学习加速方法在发现环境压力化物超导体方面非常有效.
- 发现的A2MM'H6化合物代表了对实际超导应用的有前途的新材料类.
- 该方法很容易扩展到搜索其他类型的超导化合物.
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