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 …

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 …

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 …

Although the practice of NMR spectroscopy has changed hugely over the last 20 years, the
physical principles of liquid-state NMR, with which this book is concerned, remain …

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 …

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 …

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