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 …

Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …

Conspectus Hydrogen-bonded organic frameworks (HOFs) are a class of porous molecular
materials that rely on the assembly of organic building blocks by means of hydrogen …

With 12 crystal forms, 5-methyl-2-[(2-nitrophenyl) amino]-3-thiophenecabonitrile (aka ROY)
holds the current record for the largest number of fully characterized organic crystal …

The reliability of organic molecular crystal structure prediction has improved tremendously in
recent years. Crystal structure predictions for small, mostly rigid molecules are quickly …

The combination of modern machine learning (ML) approaches with high-quality data from
quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost …

Molecular crystals are important for many applications, including energetic materials,
organic semiconductors, and the development and commercialization of pharmaceuticals …

First-principles prediction of nuclear magnetic resonance chemical shifts plays an
increasingly important role in the interpretation of experimental spectra, but the required …

Co-crystals are a highly interesting material class as varying their components and
stoichiometry in principle allows tuning supramolecular assemblies toward desired physical …

Quantum mechanics (QM) approaches (DFT, MP2, CCSD (T), etc.) play an important role in
calculating molecules and crystals with a high accuracy and acceptable efficiency. In recent …