论文标题
解码从原位显微镜观测的相变机理
Decoding the mechanisms of phase transitions from in situ microscopy observations
论文作者
论文摘要
使用可视化的域形态学的机器学习分析通过可视化的扫描传输电子显微镜（STEM）成像数据来探索了温度诱导的BATIO3中的相变。这种方法基于对系统统计描述符的时间或温度依赖性的多元统计分析，该分析又从观察到的域结构的分类分类或对特征提取差减少变换的连续参数空间的投影得出。提出的工作流提供了一个强大的工具，用于基于图像表示的统计数据来探索动态数据，这是外部控制变量的函数，以可视化相变和化学反应期间的转换途径。这可以包括此处所示的介观茎数据，还包括光学，化学成像等。它可以进一步扩展到更高的维空间，例如，分析材料组成的组合库。
Temperature-induced phase transition in BaTiO3 has been explored using the machine learning analysis of domain morphologies visualized via variable-temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction-dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc. data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.
