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 …

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 …

This paper introduces a new model to learn graph neural networks equivariant to rotations,
translations, reflections and permutations called E (n)-Equivariant Graph Neural Networks …

Many important problems involving molecular property prediction from 3D structures have
limited data, posing a generalization challenge for neural networks. In this paper, we …

Classical empirical force fields have dominated biomolecular simulation for over 50 years.
Although widely used in drug discovery, crystal structure prediction, and biomolecular …

Abstract Machine learning potentials are an important tool for molecular simulation, but their
development is held back by a shortage of high quality datasets to train them on. We …

Recent years have seen the advent of molecular simulation datasets that are orders of
magnitude larger and more diverse. These new datasets differ substantially in four aspects …

Fast and accurate calculation of intermolecular interaction energies is desirable for
understanding many chemical and biological processes, including the binding of small …

The development of reliable and extensible molecular mechanics (MM) force fields—fast,
empirical models characterizing the potential energy surface of molecular systems—is …

A wide range of density functional methods and basis sets are available to derive the
electronic structure and properties of molecules. Quantum mechanical calculations are too …