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

Abstract

Abstract 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 hierarchical 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, Pearson χ2 shows the highest mean F1 score of 0.77 and lowest standard deviation of 0.008. It shows that the distance matrixes calculated by Pearson χ2 are mainly controlled by the XRD peak shifting characteristics and visualized by the metric multidimensional data scaling.

Keywords

• combinatorial materials chip
• high‐throughput characterization
• machine learning
• metric multidimensional data scaling
• signal‐to‐noise ratio
• X‐ray techniques