A cloud platform for atomic pair distribution function analysis: PDFitc.

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

doi:10.1107/s2053273320013066

Long Yang, Simon J L Billinge + Show 6 more

Open Access

https://doi.org/10.1107/s2053273320013066

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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.

Highlights

Deploying applications in the cloud brings a number of additional benefits beyond the ease of use and ready access to high-performance computing (HPC) resources, such as linking software to databases to allow for machine learning and recommender systems, automated software updates without user installation, and supporting many different operating systems and mobile devices (Kim & Korea, 2009)
The web services make use of the pair distribution function (PDF) modeling program DiffPy-CMI (Juhas et al, 2015) and other Python packages such as TensorFlow (Abadi et al, 2016) and SciPy.stats (Jones et al, 2001). They are deployed on cloud computing services [currently the Google Cloud Platform (GCP)] using the Python Flask framework
To use structureMining, the user uploads a PDF and gets the answer back once the calculation finishes in the cloud, as we summarize below

Summary

Introduction

In the world of structure science from diffraction data, a series of reliable data analysis programs (Larson & Von Dreele, 1994; Sheldrick, 2008; Rodrıguez-Carvajal, 1993; Coelho, 2007; Altomare et al, 1999) have easy-to-use graphical user interfaces (GUIs) (Toby, 2001; Toby & Von Dreele, 2013; Pape & Schneider, 2004; Roisnel & Rodrıquez-Carvajal, 2001; Coelho, 2018; Farrugia, 1999), making the diffraction technique a more widely applied tool. We report the development of a new easy-to-use cloudbased platform called PDF in the cloud (PDFitc) for users to analyze and interpret their PDF data. It initially presents three analysis applications but more are planned in the future

PDFitc

PDFitc user interface using structureMining as an example

Conclusions

Funding information