Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering.

Analyzing large X-ray diffraction (XRD) datasets is a key step inhigh-throughput mapping of the compositional phase diagrams of combinatorialmaterials libraries. Optimizing and automating this task can help acceleratethe process of discovery of materials with novel and desirable properties.Here, we report a new method for pattern analysis and phase extraction of XRDdatasets. The method expands the Nonnegative Matrix Factorization method, whichhas been used previously to analyze such datasets, by combining it with customclustering and cross-correlation algorithms. This new method is capable ofrobust determination of the number of basis patterns present in the data which,in turn, enables straightforward identification of any possible peak-shiftedpatterns. Peak-shifting arises due to continuous change in the latticeconstants as a function of composition, and is ubiquitous in XRD datasets fromcomposition spread libraries. Successful identification of the peak-shiftedpatterns allows proper quantification and classification of the basis XRDpatterns, which is necessary in order to decipher the contribution of eachunique single-phase structure to the multi-phase regions. The process can beutilized to determine accurately the compositional phase diagram of a systemunder study. The presented method is applied to one synthetic and oneexperimental dataset, and demonstrates robust accuracy and identificationabilities.