用机器学习来区分X射线总散射的同otropic和 anisotropic信号
Danielle N Alverson1, Daniel Olds2, Megan M Butala1
1Materials Science and Engineering, University of Florida, Gainesville, FL 32611, USA.
概括
一种新方法,IsoDAT2D,使用机器学习来分析单晶基板上的薄膜X射线散射. 这有助于对新技术的结构-属性关系的理解.
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:
- 薄膜中的结构-属性关系对于技术进步至关重要.
- X射线对分布函数 (PDF) 分析揭示了多个长度尺度的原子结构.
- 由于布拉格点干扰,现有的PDF方法不适合单晶基板上的薄膜.
研究的目的:
- 开发一种新的数据处理方法,用于X射线PDF分析单晶基板上的薄膜.
- 为了克服来自单晶基质的强烈布拉格散射所带来的局限性.
- 为了能够准确地确定结构的起源,并阐明结构-属性关系.
主要方法:
- 开发IsoDAT2D,一个使用无监督机器学习 (缩小维度和集群) 的数据处理程序.
- 使用SimDAT2D验证IsoDAT2D,这是一个模拟薄膜X射线散射数据的程序.
- 应用IsoDAT2D来隔离X射线总散射信号从单晶基板上的薄膜.
主要成果:
- IsoDAT2D成功地分离了薄膜和单晶基板的X射线散射信号.
- 使用IsoDAT2D处理的PDF数据显示,与单晶基质减去方法相比,显著改善.
- 虽然不像无形基板减法那样可靠,但IsoDAT2D为单晶基板上的薄膜提供了有价值的数据.
结论:
- IsoDAT2D可以将X射线PDF分析扩展到单晶基板上的薄膜.
- 这种进步有助于阐明新的结构-财产关系.
- 预计在薄膜材料方面将有基本的理解和技术进步.
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