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

In the last century, molecular crystals functioned predominantly as a means for determining
the molecular structures via X-ray diffraction, albeit as the century came to a close the …

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Progress in the discovery of new materials has been accelerated by the development of
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …

The modular nature of metal–organic frameworks (MOFs) enables synthetic control over
their physical and chemical properties, but it can be difficult to know which MOFs would be …

Conspectus Hydrogen-bonded organic frameworks (HOFs) are a class of porous molecular
materials that rely on the assembly of organic building blocks by means of hydrogen …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …

With the development of modern society, the requirement for energy has become
increasingly important on a global scale. Therefore, the exploration of novel materials for …

Simulation techniques based on accurate and efficient representations of potential energy
surfaces are urgently needed for the understanding of complex systems such as solid–liquid …