Applications of principal component analysis to pair distribution function data

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• 作者：Karena W. Chapman ; Saul H. Lapidus and Peter J. Chupas
• 关键词：parametric ; high-throughput ; multivariate analysis ; model-independent analysis ; pair distribution function ; principal component analysis
• 刊名：Journal of Applied Crystallography
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：48
• 期：6
• 页码：1619-1626
• 全文大小：1069K
• ISSN：1600-5767

文摘

Developments in X-ray scattering instruments have led to unprecedented access to in situ and parametric X-ray scattering data. Deriving scientific insights and understanding from these large volumes of data has become a rate-limiting step. While formerly a data-limited technique, pair distribution function (PDF) measurement capacity has expanded to the point that the method is rarely limited by access to quantitative data or material characteristics – analysis and interpretation of the data can be a more severe impediment. This paper shows that multivariate analyses offer a broadly applicable and efficient approach to help analyse series of PDF data from high-throughput and in situ experiments. Specifically, principal component analysis is used to separate features from atom–atom pairs that are correlated – changing concentration and/or distance in concert – allowing evaluation of how they vary with material composition, reaction state or environmental variable. Without requiring prior knowledge of the material structure, this can allow the PDF from constituents of a material to be isolated and its structure more readily identified and modelled; it allows one to evaluate reactions or transitions to quantify variations in species concentration and identify intermediate species; and it allows one to identify the length scale and mechanism relevant to structural transformations.