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Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

Chemistry

Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

A. S. Anker, E. T. S. Kjær, et al.

Dive into the world of material science with our cutting-edge research! This paper unveils the Machine Learning based Motif Extractor (ML-MotEx), a revolutionary tool that uncovers important features for model quality in X-ray and neutron scattering studies. Conducted by a talented team from the University of Copenhagen and collaborating institutions, this work sheds light on disordered nanomaterials and clusters using advanced machine learning techniques.

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