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人工神经网络,通过扩展的X射线吸收细结构光谱学解读凝聚ZnO的结构转化
Jiangwen Liao1,2, Jiajing Pei1, Guikai Zhang1
1Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
概括
机器学习破译了X射线数据,揭示了ZnO.
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
- 晶体学 晶体学是指结晶学.
背景情况:
- 压力诱导的结构相变是新材料性质的关键.
- 了解密集机制需要精确的结构分析,这是一个挑战.
研究的目的:
- 在ZnO.中研究B4 → B1相过渡路径.
- 开发一种机器学习方法来分析X射线吸收细结构 (XAFS) 数据.
主要方法:
- 结合XAFS光谱与人工神经网络 (NN).
- NN从扩展的XAFS频谱中重建了部分辐射分布函数.
- 追踪结构统计,内部参数u,以及ZnO中的无效应.
主要成果:
- 观察到内部结构参数u的下降.
- 在B4 → B1阶段过渡开始附近检测到增加的无调性.
- 表示中间的iT阶段偏好,可能是由于剪切声模式软化.
结论:
- 基于NN的分析在复杂的现场条件下提供了XAFS数据的高效解释.
- 这种方法可以实现自动化数据处理,用于高通量表征.
- 铺平了下一代同步机设施的道路.
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