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Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering
npj Computational Materials ( IF 9.7 ) Pub Date : 2018-08-06 , DOI: 10.1038/s41524-018-0099-2
Valentin Stanev , Velimir V. Vesselinov , A. Gilad Kusne , Graham Antoszewski , Ichiro Takeuchi , Boian S. Alexandrov

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.



中文翻译:

非负矩阵分解与定制聚类相结合的X射线衍射数据的无监督相图

分析大型X射线衍射(XRD)数据集是组合材料库组成相图的高通量映射的关键步骤。优化和自动化此任务可以帮助加快发现具有新颖和理想特性的材料的过程。在这里,我们报告了一种用于XRD数据集的模式分析和相位提取的新方法。该方法通过将非负矩阵分解方法与自定义聚类和互相关算法相结合,扩展了以前用于分析此类数据集的非负矩阵分解方法。这种新方法能够可靠地确定数据中存在的基本模式的数量,从而可以直接识别任何可能的峰移模式。峰位移是由于晶格常数随成分的连续变化而产生的,并且在成分分布库的XRD数据集中无处不在。成功识别峰移图样可以对基本XRD图谱进行正确的量化和分类,这对于解密每个唯一的单相结构对多相区域的贡献是必要的。该过程可用于准确确定正在研究的系统的组成相图。所提出的方法被应用于一个合成的和一个实验的数据集,并证明了鲁棒的准确性和识别能力。成功识别峰移图样可以对基本XRD图谱进行正确的量化和分类,这对于解密每个唯一的单相结构对多相区域的贡献是必要的。该过程可用于准确确定正在研究的系统的组成相图。所提出的方法被应用于一个合成的和一个实验的数据集,并证明了鲁棒的准确性和识别能力。成功识别峰移图样可以对基本XRD图谱进行正确的量化和分类,这对于解密每个唯一的单相结构对多相区域的贡献是必要的。该过程可用于准确确定正在研究的系统的组成相图。所提出的方法被应用于一个合成的和一个实验的数据集,并证明了鲁棒的准确性和识别能力。

更新日期:2019-01-26
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