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Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

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npj Computational Materials 2018年第1期4卷 295-304页

作者： Valentin stanev Velimir V.Vesselinov A.Gilad Kusne Graham Antoszewski Ichiro Takeuchi boian s.alexandrov Department of Materials science and Engineering University of MarylandCollege ParkMD 20742USA Center for Nanophysics and Advanced Materials University of MarylandCollege ParkMD 20742USA Joint Quantum Institute University of MarylandCollege ParkMD 20742USA Los Alamos National Laboratory Earth Science DivisionLos AlamosNM 87545USA National Institute of standards and Technology GaithersburgMD 20899USA Department of Mathematics University of MarylandCollege ParkMD 20742USA Los Alamos National Laboratory Theoretical DivisionLos AlamosNM 87545USA

Analyzing large X-ray diffraction(XRD)datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials *** and automating this task can help accelerate the process of discovery of materials with novel and desirable ***,we report a new method for pattern analysis and phase extraction of XRD *** 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 *** 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 ***-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread *** 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 *** process can be utilized to determine accurately the compositional phase diagram of a system under *** presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities.

关键词： properties. phase nonnegative

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