Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

This is a review of atomic pair distribution function (PDF) analysis as applied to the study of
molecular materials. The PDF method is a powerful approach to study short-and …

There is an emerging interest in using pyrolyzed metal–organic frameworks (MOFs) for
electrocatalytic applications. While the MOF precursor and the final pyrolyzed catalyst are …

Current solid state synthesis intrinsically involves a multidimensional space which is
challenging to parametrize and predict. The diversity of extended structures comes from the …

The pair distribution function (PDF) is a versatile characterisation tool in materials science,
capable of retrieving atom–atom distances on a continuous scale (from a few angstroms to …

Abstract Characterization of material structure with X-ray or neutron scattering using eg Pair
Distribution Function (PDF) analysis most often rely on refining a structure model against an …

Determination of crystal structures of nanocrystalline or amorphous compounds is a great
challenge in solid‐state chemistry and physics. Pair distribution function (PDF) analysis of X …

Structure solution of nanostructured materials that have limited long-range order remains a
bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that …

A detailed workflow is presented for applying attenuated crystal modeling (ACM) of atomic
pair distribution functions from TiO2 nanoparticles to understand the structure and …

Heterogeneous catalysts are complex materials, often containing multiple atomic species
and phases with various degrees of structural order. The identification of structure …