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

Nowadays, many chemical investigations are supported by routine calculations of molecular
structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share …

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 …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …

Density functional theory calculations are robust tools to explore the mechanical properties
of pristine structures at their ground state but become exceedingly expensive for large …

Machine learning (ML) is a field of computer science that uses algorithms and techniques for
automating solutions to complex problems that are hard to program using conventional …

Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …

Large-area electronics for the Internet of Things requires a new generation of light-weight,
flexible, low-power electronics, based on advanced materials able to provide high …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …