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 recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Here we summarize recent progress in machine learning for the chemical sciences. We
outline machine-learning techniques that are suitable for addressing research questions in …

Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and
image search, speech recognition, as well as bioinformatics, with growing impact in …

Current learning machines have successfully solved hard application problems, reaching
high accuracy and displaying seemingly intelligent behavior. Here we apply recent …

Machine learning (ML) is transforming all areas of science. The complex and time-
consuming calculations in molecular simulations are particularly suitable for an ML …