Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions
Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions
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自下而上构建铁电体：原子级铁电畸变的机器学习分析
DOI：
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发表时间：
2019
影响因子：
4
通讯作者：
Sergei V. Kalinin
中科院分区：
文献类型：
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作者：
M. Ziatdinov;Chris Nelson;Rama Vasudevan;Deyang Chen;Sergei V. Kalinin
扫描透射电子显微镜（STEM）的最新进展使直接
铁性材料原子结构的可视化，使原子结构的测定成为可能。
列位置，精度为~pm。这反过来又使铁电体的直接映射成为可能，
铁弹序参量场通过自顶向下的方法，其中原子坐标是
直接映射到介观序参量上。在这里，我们将探索另一种自底向上的方法
方法，其中来自STEM图像的原子坐标用于探索
材料中现存的原子位移模式，并建立积木的集合
对于扭曲的晶格。这种方法被说明为La掺杂BiFeO 3系统。完整的分析过程可作为一个交互式的文件，在谷歌Colab（谷歌）笔记本电脑的形式，其中一个经典的纸组织与默认情况下出现隐藏（当在谷歌Colab查看）的代码单元增加。这应该允许读者回溯分析，更重要的是，它使读者能够对他们的数据使用相同的代码。同一篇论文也有标准的PDF格式（没有代码）。
Recent advances in scanning transmission electron microscopy (STEM) have enabled direct
visualization of the atomic structure of ferroic materials, enabling the determination of atomic
column positions with ~pm precision. This, in turn, enabled direct mapping of ferroelectric and
ferroelastic order parameter fields via the top-down approach, where the atomic coordinates are
directly mapped on the mesoscopic order parameters. Here, we explore the alternative bottom-up
approach, where the atomic coordinates derived from the STEM image are used to explore the
extant atomic displacement patterns in the material and build the collection of the building blocks
for the distorted lattice. This approach is illustrated for the La-doped BiFeO3 system.The full analysis procedure is available as an interactive paper in a form of a Google Colab (Jupyter) notebook where a classical paper organization is augmented with code cells that appear hidden by default (when viewed in Google Colab). This should allow a reader to retrace the analysis and, more importantly, it enables the readers to use the same codes for their data. The same paper is also available in a standard pdf format (without code).