The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …

Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …

Accurate simulations of atomistic systems from first principles are limited by computational
cost. In high-throughput settings, machine learning can reduce these costs significantly by …

Metal halide perovskite (MHP) is a promising next generation energy material for various
applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs …

Calculations of dislocation-defect interactions are essential to model metallic strength, but
the required system sizes are at or beyond ab initio limits. Current estimates thus have …

Physically motivated and mathematically robust atom-centered representations of molecular
structures are key to the success of modern atomistic machine learning. They lie at the …

Many atomic descriptors are currently limited by their unfavourable scaling with the number
of chemical elements S eg the length of body-ordered descriptors, such as the SOAP power …

Data-driven, or machine learning (ML), approaches have become viable alternatives to
semiempirical methods to construct interatomic potentials, due to their capacity to accurately …

Proton exchange membrane water electrolyzers (PEMWEs) have great potential as energy
conversion devices for storing renewable electricity into hydrogen energy. However, their …