Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative
structure–activity relationships to accelerate electrocatalyst design by learning from historic …

The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

This review paper describes the basic concept and technical details of sparse modeling and
its applications to quantum many-body problems. Sparse modeling refers to methodologies …

Measurement is essential in science and industry. In materials science, materials' physical
and chemical properties are measured using various probes. Synchrotron radiation (SR) …

A magnetic skyrmion, a topological magnetic structure on nanometric spatial scale, behaves
like an isolated particle in a magnetic material. Investigating the response of a skyrmion to …

This article presents a Bayesian sparse modeling method to analyze extended x-ray
absorption fine structure (EXAFS) data with basis functions built on two-body signals. This …

In this study, a new method for the phase retrieval of electron rocking curves observed using
convergent-beam electron diffraction, which is applicable to the determination of three …

In recent years, neural networks have become a new method for the analysis of extended X-
ray absorption fine structure data. Due to its sensitivity to local structure, X-ray absorption …

Compton scattering is a well-established technique that can provide detailed information
about electronic states in solids. Making use of the principle of tomography, it is possible to …

In various transition metals with different crystal structures, we have demonstrated the
validity of sparse modeling (SpM) for the analysis of extended X-ray absorption fine structure …