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

Over the past decades, Li-ion battery (LIB) has turned into one of the most important
advances in the history of technology due to its extensive and in-depth impact on our life. Its …

Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a
fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow …

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 …

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 …

Generating the periodic structure of stable materials is a long-standing challenge for the
material design community. This task is difficult because stable materials only exist in a low …

QuantumATK is an integrated set of atomic-scale modelling tools developed since 2003 by
professional software engineers in collaboration with academic researchers. While different …

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

The development of a data‐driven science paradigm is greatly revolutionizing the process of
materials discovery. Particularly, exploring novel nonlinear optical (NLO) materials with the …

Motivated by the recent report of room-temperature superconductivity at near-ambient
pressure in N-doped lutetium hydride, we performed a comprehensive, detailed study of the …