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arXiv · 2204.00434

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

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

We investigate a prototype application for machine-readable literature. 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 (doi) 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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Berrak Özer, Martin A. Karlsen, Zachary Thatcher, Ling Lan, Brian McMahon, Peter R. Strickland, Simon P. Westrip, Koh S. Sang, David G. Billing, Dorthe B. Ravnsbæk, Simon J. L. Billinge. 2022-04-01. Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern. https://arxiv.org/abs/2204.00434

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