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 …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

Structurally disordered materials pose fundamental questions,,–, including how different
disordered phases ('polyamorphs') can coexist and transform from one phase to another …

Develo** algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …

We review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …

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

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …

Progress in the discovery of new materials has been accelerated by the development of
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …

Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …