In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

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 …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

Abstract Machine-learned force fields combine the accuracy of ab initio methods with the
efficiency of conventional force fields. However, current machine-learned force fields …

Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

Force fields (FFs) form the basis of molecular simulations and have significant implications
in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is …

Free energies govern the behavior of soft and liquid matter, and improving their predictions
could have a large impact on the development of drugs, electrolytes, or homogeneous …

The discovery of new inorganic materials in unexplored chemical spaces necessitates
calculating total energy quickly and with sufficient accuracy. Machine learning models that …

A great need exists for computationally efficient quantum simulation approaches that can
achieve an accuracy similar to high-level theories at a fraction of the computational cost. In …