Search

This article demonstrates spatial mapping of the local and nanoscale structure of thin film objects using spatially resolved pair distribution function (PDF) analysis of synchrotron X‐ray diffraction data. This is exemplified in a lab‐on‐chip combinatorial array of sample spots containing catalytically interesting nanoparticles deposited from liquid precursors using an ink‐jet liquid‐handling system. A software implementation is presented of the whole protocol, including an approach for automated data acquisition and analysis using the atomic PDF method. The protocol software can handle semi‐automated data reduction, normalization and modeling, with user‐defined recipes generating a comprehensive collection of metadata and analysis results. By slicing the collection using included functions, it is possible to build images of different contrast features chosen by the user, giving insights into different aspects of the local structure. [ABSTRACT FROM AUTHOR]

Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated using a fixed set of parameters that reflect specific experimental conditions, and the accuracy of the model when given PDFs generated with different choices of these parameters is unknown. In this work, the results of the top‐1 accuracy and top‐6 accuracy are robust when applied to PDFs of different choices of experimental parameters rmax, Qmax, Qdamp and atomic displacement parameters. [ABSTRACT FROM AUTHOR]

A cloud‐hosted web‐based software application, nmfMapping, for carrying out a non‐negative matrix factorization of a set of powder diffraction or atomic pair distribution function datasets is described. This application allows structure scientists to find trends rapidly in sets of related data such as from in situ and operando diffraction experiments. The application is easy to use and does not require any programming expertise. It is available at https://pdfitc.org/. [ABSTRACT FROM AUTHOR]