%0 Journal Article
%A Kjær, Emil T. S.
%A Anker, Andy S.
%A Kirsch, Andrea
%A Lajer, Joakim
%A Aalling-Frederiksen, Olivia
%A Billinge, Simon J. L.
%A Jensen, Kirsten Marie
%T MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions
%J Digital discovery
%V 3
%N 5
%@ 2635-098X
%C Washington DC
%I Royal Society of Chemistry
%M PUBDB-2024-05800
%P 908-918
%D 2024
%X Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis, structure, and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day. Such amounts of data can speed up materials development, but also comes with a staggering growth in workload, as the data generated must be stored and analyzed. We present an approach for quickly identifying an atomic structure model from pair distribution function (PDF) data from (nano)crystalline materials. Our model, MLstructureMining, uses a tree-based machine learning (ML) classifier. MLstructureMining has been trained to classify chemical structures from a PDF and gives a top-3 accuracy of 99
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 38756225
%U <Go to ISI:>//WOS:001196386100001
%R 10.1039/D4DD00001C
%U https://bib-pubdb1.desy.de/record/614260