The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

Chalcogenide perovskites have recently emerged into the spotlight as highly robust, earth
abundant, and nontoxic candidates for various energy conversion applications, not least …

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Graph neural networks for crystal structures typically use the atomic positions and the atomic
species as input. Unfortunately, this information is not available when predicting new …

Perovskite oxynitrides (PONs) are a promising class of materials for applications ranging
from catalysis to photovoltaics. However, the vast space of single PON materials (ABO3–x N …

The doping of foreign cations and anions is one of the effective strategies for engineering
defects and modulating the optical, electronic, and surface properties that directly govern the …

Photocatalytic water splitting represents a very promising but at the same time challenging
contribution to a clean and renewable route to produce hydrogen fuel. Developing efficient …

Experimental science is enabled by the combination of synthesis, imaging, and functional
characterization organized into evolving discovery loop. Synthesis of new material is …

Short-range ordering in cation-disordered cathodes can have a significant effect on their
electrochemical properties. Here, we characterise the cation short-range order in the …

Resolving anion configurations in heteroanionic materials is crucial for understanding and
controlling their properties. For anion-disordered oxyfluorides, conventional Bragg diffraction …