We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Atomic‐scale modeling and understanding of materials have made remarkable progress,
but they are still fundamentally limited by the large computational cost of explicit electronic …

Background Disordered compounds are crucially important for fundamental science and
industrial applications. Yet most available methods to explore solid-state material properties …

Amorphous silicon (a-Si) is a widely studied noncrystalline material, and yet the subtle
details of its atomistic structure are still unclear. Here, we show that accurate structural …

Interest in molecular crystals has grown thanks to their relevance to pharmaceuticals,
organic semiconductor materials, foods, and many other applications. Electronic structure …

Solid-state nuclear magnetic resonance (NMR) spectroscopy is an atomic-level method to
determine the chemical structure, 3D structure and dynamics of solids and semi-solids. This …

Density functional theory (DFT) has been used in many fields of the physical sciences, but
none so successfully as in the solid state. From its origins in condensed matter physics, it …

Developments of NMR methodology to characterise the structures of molecular organic
structures are reviewed, concentrating on the previous decade of research in which density …

The chemical order and disorder of solids have a decisive influence on the material
properties. There are numerous materials exhibiting chemical order/disorder of atoms with …

Hybrid organic–inorganic perovskites represent a special class of metal–organic framework
where a molecular cation is encased in an anionic cage. The molecule–cage interaction …