Effect of signal-to-noise ratio on the automatic clustering of X-ray diffraction patterns from combinatorial libraries  

作　　者：Yuanxun Zhou Biao Wu Jianhao Wang Hong Wang 

机构地区：[1]School of Materials Science and Engineering,Shanghai Jiao Tong University,Shanghai,China [2]Materials Genome Initiative Center,Shanghai Jiao Tong University,Shanghai,China [3]Shanghai Key Laboratory of High-Temperature Materials and Precision Forming,Shanghai Jiao Tong University,Shanghai,China [4]Zhangjiang Institute for Advanced Study,Shanghai Jiao Tong University,Shanghai,China

出　　处：《Materials Genome Engineering Advances》2024年第1期97-103,共7页材料基因工程前沿（英文）

基　　金：funded by the National Key Research and Development Program of China(Grant Nos.2021YFB370-2102 and 2017YFB0701900).

摘　　要：Hierarchical clustering algorithm has been applied to identify the X-ray diffraction(XRD)patterns from a high-throughput characterization of the combinatorial materials chips.As data quality is usually correlated with acquisition time,it is important to study the hierarchical clustering performance as a function of data quality in order to optimize the efficiency of high-throughput experiments.This work investigated the effects of signal-to-noise ratio on the performance of hier-archical clustering using 29 distance metrics for the XRD patterns from Fe−Co−Ni ternary combinatorial materials chip.It is found that the clustering accuracies evaluated by the F1 score only fluctuate slightly with signal-to-noise ratio varying from 15.5 to 22.3(dB)under the experimental condition.This suggests that although it may take 40-50 s to collect a visually high-quality diffraction pattern,the measurement time could be significantly reduced to as low as 4 s without substantial loss in phase identification accuracy by hierarchical clustering.Among the 29 distance metrics,Pearsonχ^(2)shows the highest mean F1 score of 0.77 and lowest standard deviation of 0.008.It shows that the distance matrixes calculated by Pearsonχ^(2)are mainly controlled by the XRD peak shifting characteristics and visualized by the metric multidimensional data scaling.

关 键 词：combinatorial materials chip high-throughput characterization machine learning metric multidimensional data scaling signal-to-noise ratio X-ray techniques 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TB3[自动化与计算机技术—计算机科学与技术]