Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …

The computer simulation of condensed systems is a challenging task. While electronic
structure methods like density-functional theory (DFT) usually provide a good compromise …

Multi-principal element solid solutions are prone to develop local chemical inhomogeneities,
ie, chemical order/clustering and/or compositional undulation. However, these structural …

Alloy cluster expansions (CEs) provide an accurate and computationally efficient map** of
the potential energy surface of multi‐component systems that enables comprehensive …

2D transition metal carbides and nitrides known as MXenes are gaining increasing attention.
About 20 of them have been synthesized (more predicted) and their applications in fields …

Over the past two decades, the necessity for predictive models of chemical kinetics on
catalytic surfaces has motivated the development of ab initio kinetic Monte Carlo (KMC) …

The widely accepted intuition that the important properties of solids are determined by a few
key variables underpins many methods in physics. Though this reductionist paradigm is …

Hamiltonian learning, as an important quantum machine learning technique, provides a
significant approach for determining an accurate quantum system. This paper establishes …

Materials discovery is decisive for tackling urgent challenges related to energy, the
environment, health care, and many others. In chemistry, conventional methodologies for …