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A cloud platform for atomic pair distribution function analysis: PDFitc
Author(s) -
Yang Long,
Culbertson Elizabeth A.,
Thomas Nancy K.,
Vuong Hung T.,
Kjær Emil T. S.,
Jensen Kirsten M. Ø.,
Tucker Matthew G.,
Billinge Simon J. L.
Publication year - 2021
Publication title -
acta crystallographica section a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.742
H-Index - 83
ISSN - 2053-2733
DOI - 10.1107/s2053273320013066
Subject(s) - upload , computer science , workflow , cloud computing , data mining , set (abstract data type) , function (biology) , data set , data science , database , world wide web , operating system , artificial intelligence , programming language , evolutionary biology , biology
A cloud web platform for analysis and interpretation of atomic pair distribution function (PDF) data ( PDFitc ) is described. The platform is able to host applications for PDF analysis to help researchers study the local and nanoscale structure of nanostructured materials. The applications are designed to be powerful and easy to use and can, and will, be extended over time through community adoption and development. The currently available PDF analysis applications, structureMining, spacegroupMining and similarityMapping , are described. In the first and second the user uploads a single PDF and the application returns a list of best‐fit candidate structures, and the most likely space group of the underlying structure, respectively. In the third, the user can upload a set of measured or calculated PDFs and the application returns a matrix of Pearson correlations, allowing assessment of the similarity between different data sets. structureMining is presented here as an example to show the easy‐to‐use workflow on PDFitc . In the future, as well as using the PDFitc applications for data analysis, it is hoped that the community will contribute their own codes and software to the platform.