Analyzing large X-ray diffraction (XRD) datasets is a key step in
high-throughput mapping of the compositional phase diagrams of combinatorial
materials libraries. Optimizing and automating this task can help accelerate
the process of discovery of materials with novel and desirable properties.
Here, we report a new method for pattern analysis and phase extraction of XRD
datasets. The method expands the Nonnegative Matrix Factorization method, which
has been used previously to analyze such datasets, by combining it with custom
clustering and cross-correlation algorithms. This new method is capable of
robust determination of the number of basis patterns present in the data which,
in turn, enables straightforward identification of any possible peak-shifted
patterns. Peak-shifting arises due to continuous change in the lattice
constants as a function of composition, and is ubiquitous in XRD datasets from
composition spread libraries. Successful identification of the peak-shifted
patterns allows proper quantification and classification of the basis XRD
patterns, which is necessary in order to decipher the contribution of each
unique single-phase structure to the multi-phase regions. The process can be
utilized to determine accurately the compositional phase diagram of a system
under study. The presented method is applied to one synthetic and one
experimental dataset, and demonstrates robust accuracy and identification
abilities.

在高通量材料库的组成相图繁显中，X 射线衍射数据集的分析是一个关键步骤，本论文提出了一种将非负矩阵分解方法与自定义聚类和交叉相关算法相结合的方法，能够成功鉴别数据中所有可能的峰移 XRD 基本图案，并能够精确地确定系统的组成相图。