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DOE OSTI · 2470304

Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern

Özer, Berrak·Karlsen, Martin A.·Thatcher, Zachary·Lan, Ling·McMahon, Brian·Strickland, Peter R.·Westrip, Simon P.·Sang, Koh S.·Billing, David G.·Ravnsbæk, Dorthe B.·Billinge, Simon J. L.
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Abstract

A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRYCIFChemistryCrystallographydata similaritydata-driven literature searchmachine-readable scientific literaturepowder diffraction

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Özer, Berrak, Karlsen, Martin A., Thatcher, Zachary, Lan, Ling, McMahon, Brian, Strickland, Peter R., Westrip, Simon P., Sang, Koh S., Billing, David G., Ravnsbæk, Dorthe B., Billinge, Simon J. L.. 2022-08-19. Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern. https://doi.org/10.1107/s2053273322007483

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