使用机器学习在电子衍射中确定晶体对称性

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Updated: Dec 29, 2025

$Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography$

Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography

Published on: April 24, 2018

Kevin Kaufmann¹, Chaoyi Zhu², Alexander S Rosengarten¹

¹Department of NanoEngineering, University of California, San Diego, La Jolla, CA 92093, USA.

Science (New York, N.Y.)

|February 1, 2020

概括

我们开发了一种自动化的机器学习方法, 这种方法可以快速,自主地识别相位,从而推进EBSD作为高通量技术.

科学领域:

材料科学
晶体学
机器学习

背景情况:

电子反射散射 (EBSD) 对于晶体结构的确定至关重要,但需要手动输入用于相位识别.
由于依赖于人类的解释,目前的EBSD方法并没有优化高吞吐量分析.

研究的目的:

从EBSD模式中快速和自主识别晶体对称性的一般方法.
为了实现自动化阶段识别,将EBSD转化为高通量技术.

主要方法:

使用基于机器学习的方法来分析EBSD模式.
开发了一个用于自主晶体对称识别的神经网络算法.

主要成果:

该算法成功地从EBSD模式中识别了晶体对称性.
在未包括在训练组中的衍射模式上评估算法的性能.
神经网络优先考虑的对称性特征与晶体学家的分析一致.

结论:

开发的机器学习方法有助于自主识别EBSD阶段.
这种方法显著提高了EBSD作为高通量表征技术的潜力.

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