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 …

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 …

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 …

Modern simulation techniques have reached a level of maturity which allows a wide range of
problems in chemistry and materials science to be addressed. Unfortunately, the application …

Atomic‐scale modeling and understanding of materials have made remarkable progress,
but they are still fundamentally limited by the large computational cost of explicit electronic …

We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid
and amorphous elemental carbon. Based on a machine learning representation of the …

Atomistic simulations of chemical, biological and materials systems have become
increasingly precise and predictive owing to the development of accurate and efficient …

We present an accurate machine learning (ML) model for atomistic simulations of carbon,
constructed using the Gaussian approximation potential (GAP) methodology. The potential …

A lot of progress has been made in recent years in the development of atomistic potentials
using machine learning (ML) techniques. In contrast to most conventional potentials, which …