Chemistrynpj Computational Materials
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.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Computer Science
Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing
D. Rankin, M. Black, et al.
Physics
Machine learning using structural representations for discovery of high temperature superconductors
L. Novakovic, A. Salamat, et al.
Engineering and Technology
Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
Y. Zhang, Q. Tang, et al.
Engineering and Technology
Rapid and flexible segmentation of electron microscopy data using few-shot machine learning
S. Akers, E. Kautz, et al.
