使用隐式神经表示来捕捉动态相关性
Sathya R Chitturi1,2, Zhurun Ji3,4, Alexander N Petsch5,6,7
1SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. chitturi@stanford.edu.
Nature communications
|September 20, 2023
概括
这项研究引入了一种机器学习工具,用于分析材料激发光谱. 它精确地从实验数据中提取磁交换参数,推进有序磁系统的研究.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
背景情况:
- 材料中的集体激发是理解多体物理学的关键.
- 动态结构因子 (S(Q, ω)) 通常使用不弹性中子或X射线散射来测量.
- 分析包括将实验数据与理论预测进行比较.
研究的目的:
- 开发用于光谱测量的数据驱动分析工具.
- 通过自动分化,从实验数据中高效地提取未知的参数.
- 为了使有序的磁系统能够精确地提取参数.
主要方法:
- 使用针对光谱数据量身定制的神经隐性表示.
- 在模型训练中使用线性自旋波理论模拟.
- 应用自动区分进行参数精细化.
主要成果:
- 从不弹性中子散射数据中精确提取交换参数.
- 在正方形格子旋转-1反铁磁体La2NiO4.4上成功应用.
- 展示用于高级模型改进的机器学习平台.
结论:
- 开发的工具提供了一个可行的途径,用于在有序磁系统中自动改进模型.
- 神经隐含表示显示了分析复杂物质激发光谱的前景.
- 这种数据驱动的方法增强了对材料中的磁现象的理解.
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