使用DFT和深度学习的高压化物超导体的数据驱动设计
Daniel Wines1, Kamal Choudhary1
1Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
Materials futures
|June 6, 2024
概括
研究人员使用计算方法来预测新的高压化物超导体. 他们发现了122个具有高临界温度的稳定结构,加速了材料的发现.
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:
- 在高压下化物材料的超导性引起了人们的极大兴趣.
- 发现新的超导体通常依赖于实验合成和表征,这可能是耗时和资源密集的.
- 需要采用数据驱动的方法来加速寻找新型高压化物超导体.
研究的目的:
- 在高压下计算预测900多种化物材料的临界温度 (Tc).
- 确定具有Tc值超过MgB2 (39K) 的动态稳定的化物结构.
- 开发和应用机器学习模型,以加速对潜在超导体进行选.
主要方法:
- 密度函数理论 (DFT) 的计算被用来预测许多化物材料的临界温度 (Tc).
- 图形神经网络 (GNN) 模型被训练来预测Tc,从而实现快速查.
- 一个通用机器学习的力场被用于在不同压力下高效的结构松.
主要成果:
- 确定了122个动态稳定的化物结构,预测Tc值在0至500GPa的压力范围内高于39K.
- 该GNN模型在加速Tc的预测方面表现出有效性.
- 机器学习显著降低了与结构放松相关的计算成本.
结论:
- DFT和GNN的组合为绘制高压化物超导体提供了一种强大而高效的方法.
- 这一数据驱动的策略加速了对具有潜在超导特性的新材料的发现过程.
- 这项研究为超导性研究在压力下对化物行为建立了更全面的理解.
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