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

Stanev, Valentin; Vesselinov, Velimir V.; Kusne, A. Gilad; Antoszewski, Graham; Takeuchi, Ichiro; Alexandrov, Boian S.

doi:10.1038/s41524-018-0099-2

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

Journal Article · Mon Aug 06 00:00:00 EDT 2018 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-018-0099-2· OSTI ID:1462297

Stanev, Valentin; Vesselinov, Velimir V.; Kusne, A. Gilad; Antoszewski, Graham; Takeuchi, Ichiro;

Abstract 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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of Maryland, College Park, MD (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); LANL Laboratory Directed Research and Development (LDRD) Program; Office of Naval Research (ONR) (United States); National Science Foundation (NSF)

Grant/Contract Number:: AC52-06NA25396; N00014-13-1-0635; 5289230; N000141512222; DMR-1505103

OSTI ID:: 1462297

Alternate ID(s):: OSTI ID: 1475339

Report Number(s):: LA-UR-18-20888; 43; PII: 99

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Vol. 4 Journal Issue: 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 28 works

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