A cloud platform for atomic pair distribution function analysis: PDFitc

Long Yang, Elizabeth A. Culbertson, Nancy K. Thomas, Hung T. Vuong, Emil T.S. Kjær, Kirsten M.Ø. Jensen, Matthew G. Tucker, Simon J.L. Billinge

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

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.

Original languageEnglish
Article numberae5091
JournalActa Crystallographica Section A: Foundations and Advances
Volume77
Issue numberPart1
DOIs
StatePublished - 2021

Funding

Work in the SJLB group was supported by the US National Science Foundation through grant DMREF-1534910. LY and MGT acknowledge support from the ORNL Graduate Opportunity (GO) program, which was funded by the Neutron Science Directorate, with support from the Scientific User Facilities Division, Office of Basic Energy Science, US Department of Energy (DOE). KMØJ and ETSK acknowledge funding from the European Research Council. This work is part of a project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (grant agreement No. 804066). Work in the SJLB group was supported by the US National Science Foundation through grant DMREF-1534910. LY and MGT acknowledge support from the ORNL Graduate Opportunity (GO) program, which was funded by the Neutron Science Directorate, with support from the Scientific User Facilities Division, Office of Basic Energy Science, US Department of Energy (DOE). KMØJ and ETSK acknowledge funding from the European Research Council. This work is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No. 804066).

Keywords

  • Cloud computing
  • Data analysis
  • PDF
  • Pair distribution function
  • Web applications

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