Xiong Zheng, He Yinyan, Hattrick-Simpers Jason R, Hu Jianjun
School of Mechanical Engineering, Guizhou University , Guiyang, Guizhou 550025, China.
ACS Comb Sci. 2017 Mar 13;19(3):137-144. doi: 10.1021/acscombsci.6b00121. Epub 2017 Feb 10.
The creation of composition-processing-structure relationships currently represents a key bottleneck for data analysis for high-throughput experimental (HTE) material studies. Here we propose an automated phase diagram attribution algorithm for HTE data analysis that uses a graph-based segmentation algorithm and Delaunay tessellation to create a crystal phase diagram from high throughput libraries of X-ray diffraction (XRD) patterns. We also propose the sample-pair based objective evaluation measures for the phase diagram prediction problem. Our approach was validated using 278 diffraction patterns from a Fe-Ga-Pd composition spread sample with a prediction precision of 0.934 and a Matthews Correlation Coefficient score of 0.823. The algorithm was then applied to the open Ni-Mn-Al thin-film composition spread sample to obtain the first predicted phase diagram mapping for that sample.
目前，构建成分 - 处理 - 结构关系是高通量实验（HTE）材料研究数据分析的关键瓶颈。在此，我们提出一种用于HTE数据分析的自动相图归因算法，该算法使用基于图的分割算法和德劳内三角剖分，从X射线衍射（XRD）图案的高通量库中创建晶体相图。我们还针对相图预测问题提出了基于样本对的客观评估方法。我们使用来自Fe - Ga - Pd成分扩展样品的278个衍射图案对我们的方法进行了验证，预测精度为0.934，马修斯相关系数得分为0.823。然后将该算法应用于开放的Ni - Mn - Al薄膜成分扩展样品，以获得该样品的首个预测相图映射。
