Journal Article

FZJ-2024-04640

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Learning from virtual experiments to assist users of Small Angle Neutron Scattering in model selection

Robledo, J. I. (Corresponding author)FZJ* ; Frielinghaus, H.FZJ* ; Willendrup, P. ; Lieutenant, K.FZJ*

2024
Macmillan Publishers Limited, part of Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41598-024-65712-y  doi:10.34734/FZJ-2024-04640

Abstract: In this work, we combine the advantages of virtual Small Angle Neutron Scattering (SANS) experiments carried out by Monte Carlo simulations with the recent advances in computer vision to generate a tool that can assist SANS users in small angle scattering model selection. We generate a dataset of almost 260.000 SANS virtual experiments of the SANS beamline KWS-1 at FRM-II, Germany, intended for Machine Learning purposes. Then, we train a recommendation system based on an ensemble of Convolutional Neural Networks to predict the SANS model from the two-dimensional scattering pattern measured at the position-sensitive detector of the beamline. The results show that the CNNs can learn the model prediction task, and that this recommendation system has a high accuracy in the classification task on 46 different SANS models. We also test the network with real data and explore the outcome. Finally, we discuss the reach of counting with the set of virtual experimental data presented here, and of such a recommendation system in the SANS user data analysis procedure.

Keyword(s): Information and Communication (1st) ; Instrument and Method Development (2nd)

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Contributing Institute(s):

1. JCNS-FRM-II (JCNS-FRM-II)
2. Streumethoden (JCNS-2)
3. JCNS-4 (JCNS-4)
4. Heinz Maier-Leibnitz Zentrum (MLZ)

Research Program(s):

1. 6G4 - Jülich Centre for Neutron Research (JCNS) (FZJ) (POF4-6G4) (POF4-6G4)
2. 632 - Materials – Quantum, Complex and Functional Materials (POF4-632) (POF4-632)

Experiment(s):

1. KWS-1: Small angle scattering diffractometer (NL3b)

Appears in the scientific report 2024

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