npj：数据分析——多维属性的可视化图表

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科学论文的写作少不了用图表对重要数据进行展示，以图表形式展示材料属性之间的关系，既能突出重要关系，又能增强对材料行为的理解，也方便对材料作出选择。不过，许多情况下，这些相关性本质上是高度多维的，通常只能使用二维图表分析来建立不同属性之间的关系，可能仅表达了这些关系的某些方面。要使材料的高维数据可视化、可作有意义的比较分析困难不小。来自美国Lehigh大学的Jeffrey Rickman，采用可视化策略（即平行坐标）的数据分析方法，更好地展示了多维材料数据，可以更好地识别不同属性之间的有用关系。他以这种方法，构建了金属系和陶瓷系多维材料属性图表，并作了系统分析，简化了高维几何图形的描述，实现了尺寸缩小和重要属性之间关系的识别，强化了不同材料类别之间的区别，为识别材料各属性之间的关系提供了强有力的工具。该文近期发表于npj Computational Materials 4: 5 (2018);  doi:10.1038 /s41524-017-0061-8。英文标题与摘要如下，点击阅读原文可以自由获取论文PDF。

Data analytics and parallel-coordinate materials property charts 

Jeffrey M.Rickman

It is often advantageous to display material properties relationships in the form of charts that highlight important correlations and thereby enhance our understanding of materials behavior and facilitate materials selection. Unfortunately, in many cases, these correlations are highly multidimensional in nature, and one typically employs low-dimensional cross-sections of the property space to convey some aspects of these relationships. To overcome some of these difficulties, in this work we employ methods of data analytics in conjunction with a visualization strategy, known as parallel coordinates, to represent better multidimensional materials data and to extract useful relationships among properties. We illustrate the utility of this approach by the construction and systematic analysis of multidimensional materials properties charts for metallic and ceramic systems. These charts simplify the description of high-dimensional geometry, enable dimensional reduction and the identification of significant property correlations and underline distinctions among different materials classes.

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