A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …

The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven
interatomic potentials with a formally complete basis set. Since the development of any …

Being rich in hydrogen and easy to transport, ammonia is a promising hydrogen carrier.
However, a microscopic characterization of the ammonia cracking reaction is still lacking …

Deep learning has emerged as a promising paradigm to give access to highly accurate
predictions of molecular and material properties. A common short-coming shared by current …

We develop an automated high-throughput workflow for calculating lattice dynamical
properties from first principles including those dictated by anharmonicity. The pipeline …

Solvent effects influence all stages of the chemical processes, modulating the stability of
intermediates and transition states, as well as altering reaction rates and product ratios …

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from
energy storage to solvents, where they have been touted as “designer solvents” as they can …

Quantum chemical simulations can be greatly accelerated by constructing machine learning
potentials, which is often done using active learning (AL). The usefulness of the constructed …

The emergence of artificial intelligence is profoundly impacting computational chemistry,
particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional …