Learning from virtual experiments to assist users of Small Angle Neutron Scattering in model selection.

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

SUBMITTER: Robledo JI 

PROVIDER: S-EPMC11217390 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature