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 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 …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …

Recent advances in machine-learning interatomic potentials have enabled the efficient
modeling of complex atomistic systems with an accuracy that is comparable to that of …

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 …

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 …

Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …

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 …

The correct implementation of the quantum mechanics (QM) calculation for COSMO-SAC
based surface charge density profiles (σ-profile) and cavity volumes (V COSMO) is tricky and …

Materials for solid-state batteries often exhibit complex chemical compositions, defects, and
disorder, making both experimental characterization and direct modeling with first principles …