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

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 …

The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

Deep learning is revolutionizing many areas of science and technology, especially image,
text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

The subject of this paper is the technology (the'how') of constructing machine-learning
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …

Machine learning of the quantitative relationship between local environment descriptors and
the potential energy surface of a system of atoms has emerged as a new frontier in the …

Density functional theory offers a very accurate way of computing materials properties from
first principles. However, it is too expensive for modeling large-scale molecular systems …

Determining the stability of molecules and condensed phases is the cornerstone of atomistic
modeling, underpinning our understanding of chemical and materials properties and …

The success of first-principles electronic-structure calculation for predictive modeling in
chemistry, solid-state physics, and materials science is constrained by the limitations on …