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

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 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 …

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 …

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

The development of interatomic potentials employing artificial neural networks has seen
tremendous progress in recent years. While until recently the applicability of neural network …

The accurate description of chemical processes often requires the use of computationally
demanding methods like density-functional theory (DFT), making long simulations of large …

Highly optimized embedded-atom-method (EAM) potentials have been developed for 14
face-centered-cubic (fcc) elements across the periodic table. The potentials were developed …

We present an analytical bond-order potential for silicon, carbon, and silicon carbide that
has been optimized by a systematic fitting scheme. The functional form is adopted from a …

We study in a systematic way the complex sequence of the high-pressure phases of silicon
obtained upon compression by combining an accurate high-dimensional neural network …