MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions
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MLstructureMining : a machine learning tool for structure identification from X-ray pair distribution functions. / Kjær, Emil T.S.; Anker, Andy S.; Kirsch, Andrea; Lajer, Joakim; Aalling-Frederiksen, Olivia; Billinge, Simon J.L.; Jensen, Kirsten M.Ø.
I: Digital Discovery, Bind 3, Nr. 5, 2024, s. 908–918.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - MLstructureMining
T2 - a machine learning tool for structure identification from X-ray pair distribution functions
AU - Kjær, Emil T.S.
AU - Anker, Andy S.
AU - Kirsch, Andrea
AU - Lajer, Joakim
AU - Aalling-Frederiksen, Olivia
AU - Billinge, Simon J.L.
AU - Jensen, Kirsten M.Ø.
N1 - Publisher Copyright: © 2024 RSC.
PY - 2024
Y1 - 2024
N2 - 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% on simulated PDFs not seen during training, with a total of 6062 possible classes. We also demonstrate that MLstructureMining can identify the chemical structure from experimental PDFs from nanoparticles of CoFe2O4 and CeO2, and we show how it can be used to treat an in situ PDF series collected during Bi2Fe4O9 formation. Additionally, we show how MLstructureMining can be used in combination with the well-known methods, principal component analysis (PCA) and non-negative matrix factorization (NMF) to analyze data from in situ experiments. MLstructureMining thus allows for real-time structure characterization by screening vast quantities of crystallographic information files in seconds.
AB - 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% on simulated PDFs not seen during training, with a total of 6062 possible classes. We also demonstrate that MLstructureMining can identify the chemical structure from experimental PDFs from nanoparticles of CoFe2O4 and CeO2, and we show how it can be used to treat an in situ PDF series collected during Bi2Fe4O9 formation. Additionally, we show how MLstructureMining can be used in combination with the well-known methods, principal component analysis (PCA) and non-negative matrix factorization (NMF) to analyze data from in situ experiments. MLstructureMining thus allows for real-time structure characterization by screening vast quantities of crystallographic information files in seconds.
U2 - 10.1039/d4dd00001c
DO - 10.1039/d4dd00001c
M3 - Journal article
C2 - 38756225
AN - SCOPUS:85189508889
VL - 3
SP - 908
EP - 918
JO - Digital Discovery
JF - Digital Discovery
SN - 2635-098X
IS - 5
ER -
ID: 389362291