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 …

Recent advances in machine-learning interatomic potentials have enabled the efficient
modeling of complex atomistic systems with an accuracy that is comparable to that of …

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 …

Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …

Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and
structure of matter. Since the resolution of MD is atomic-scale, achieving long timescale …

Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …

Many molecular design tasks benefit from fast and accurate calculations of quantum-
mechanical (QM) properties. However, the computational cost of QM methods applied to …

Recent advances in equivariant graph neural networks (GNNs) have made deep learning
amenable to develo** fast surrogate models to expensive ab initio quantum mechanics …

We present hierarchical machine learning (hML) of highly accurate potential energy
surfaces (PESs). Our scheme is based on adding predictions of multiple Δ-machine learning …