title: Modeling Pair Distribution Functions of Rare-Earth Phosphate Glasses Using Principal Component Analysis.
abstract: The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, codoped with two rare-earth ions (R and R') of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of codoped REPGs presents significant challenges relative to their singly doped counterparts; specifically, R and R' are difficult to distinguish in terms of establishing relative material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown codoped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are prevalidated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. While this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials and be exploited in large-scale data-mining efforts that probe many t(r) functions.
authors.affiliation: Cavendish Laboratory, University of Cambridge , J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.|Argonne National Laboratory , 9700 South Cass Avenue, Argonne, Illinois 60439, United States.|ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus , Didcot, Oxfordshire OX11 0QX, U.K.|Department of Chemical Engineering and Biotechnology, University of Cambridge , West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0FS, U.K.
authors.forename: Jacqueline M
authors.initials: JM
authors.lastname: Cole
authors.affiliation: Cavendish Laboratory, University of Cambridge , J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
authors.forename: Xie
authors.initials: X
authors.lastname: Cheng
authors.affiliation: Cavendish Laboratory, University of Cambridge , J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Payne