以物理为基础的机器学习分析,用于通过同步机 Laue 微微衍射的纳米尺度谷物映射
Ka Hung Chan1,2, Xinyue Huang1, Nobumichi Tamura2
1Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Hong Kong.
概括
一种新的基于物理的机器学习 (PIML) 方法增强了同步龙X射线微差别. 这种方法实现了纳米晶体的纳米粒度映射分辨率,克服了以前的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术纳米技术
- 晶体学 晶体学是指结晶学.
背景情况:
- 对先进材料来说,确定纳米晶体的特性至关重要.
- 同步射线X射线Laue微分歧是晶体结构和方向映射的关键.
- 微微差分的空间分辨率有限,阻碍了对子束大小的颗粒进行映射.
研究的目的:
- 开发一种新的方法,用于高分辨率的纳米晶体的粒度形态绘制.
- 为了克服X射线微微衍射技术的空间分辨率限制.
- 为了使纳米级材料的特征使用衍射探针.
主要方法:
- 开发了一种基于物理的机器学习 (PIML) 方法.
- 该PIML方法集成一个卷积神经网络与物理知情过.
- 在金 (Au) 纳米晶体上进行了同步离子微衍射扫描.
主要成果:
- 通过PIML方法实现了用于谷物映射的纳米分辨率.
- 证明了精确的粒度大小,方向分布和形态的确定.
- 结果与传统的电子反射散射衍射 (EBSD) 数据有很好的一致性.
结论:
- 用PIML辅助的微分离成功地以纳米级分辨率映射了纳米晶粒.
- 这种技术克服了X射线探测器尺寸对亚微米材料的限制.
- 基于PIML的方法可以将其推广到基于衍射的其他表征方法.
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