Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …

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 …

Neural networks (NNs) often assign high confidence to their predictions, even for points far
out of distribution, making uncertainty quantification (UQ) a challenge. When they are …

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 …

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 …

Statistical learning algorithms provide a generally-applicable framework to sidestep time-
consuming experiments, or accurate physics-based modeling, but they introduce a further …

Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is
azobenzene, which exhibits trans–cis isomerism in response to light. The thermal half-life of …

Machine learning potentials have become an essential tool for atomistic simulations,
yielding results close to ab initio simulations at a fraction of computational cost. With recent …

Data-driven methods based on machine learning have the potential to accelerate
computational analysis of atomic structures. In this context, reliable uncertainty estimates are …