Symmetry-aware recursive image similarity exploration for materials microscopy
Symmetry-aware recursive image similarity exploration for materials microscopy
复制标题
DOI：
10.1038/s41524-021-00637-y
复制
发表时间：
2021-10
影响因子：
9.7
通讯作者：
Tri Nguyen;Yichen Guo;Shuyu Qin;Kylie S. Frew;R. Xu;J. Agar
中科院分区：
文献类型：
--
作者：
Tri Nguyen;Yichen Guo;Shuyu Qin;Kylie S. Frew;R. Xu;J. Agar
在追求科学发现的过程中，获得了大量的非结构化结构和功能图像；然而，这些数据中只有极小的一部分得到了严格的分析，而被发表的数据则更少。加速科学发现的一种方法是从已经进行的昂贵的科学实验中提取更多的见解。不幸的是，科学实验的数据往往只有知道实验和指令的发起者才能获得。此外，没有强大的方法来搜索非结构化的图像数据库来推断相关性和洞察力。在这里，我们开发了一种机器学习方法来创建图像相似性投影来搜索非结构化图像数据库。为了改进这些投影，我们开发并训练了一个包含对称感知特征的模型。作为一个例子，我们使用了一组25,133张在五年内收集的不同材料系统的压电响应力显微镜图像。我们演示了该工具如何用于交互式递归图像搜索和探索，突出了不同长度尺度上的结构相似性。该工具证明继续投资具有标准化元数据模式的联邦科学数据库是合理的，在这些数据库中，过滤和递归交互式搜索的组合可以揭示合成-结构-属性关系。我们提供了这个交互式工具的一个可定制的开源包（https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer），供研究人员使用他们的数据。
In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package (https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer) of this interactive tool for researchers to use with their data.