非负矩阵分解结合自定义聚类的X射线衍射数据无监督相位映射
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
摘要：
分析大的X射线衍射（XRD）数据集是组合材料库的组成相图的高通量映射的关键步骤。优化和自动化这项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，我们报告了一种新的方法，用于模式分析和相提取的XRD数据集。该方法扩展了非负矩阵分解方法，该方法以前曾用于分析此类数据集，通过将其与自定义聚类和互相关算法相结合。这种新方法能够鲁棒地确定数据中存在的基础模式的数量，这反过来又能够直接识别任何可能的峰移模式。峰移的出现是由于晶格常数作为组成的函数的连续变化，并且在来自组成分布库的XRD数据集中是普遍存在的。峰移图案的成功识别允许基础XRD图案的适当量化和分类，这是必要的，以便破译每个独特的单相结构对多相区域的贡献。该方法可用于准确测定所研究体系的组成相图。所提出的方法被应用到一个合成和一个实验数据集，并表现出强大的准确性和识别能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
