Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …

The rapid progress of machine learning interatomic potentials over the past couple of years
produced a number of new architectures. Particularly notable among these are the Atomic …

Artificial neural networks (NNs) are one of the most frequently used machine learning
approaches to construct interatomic potentials and enable efficient large-scale atomistic …

Developing machine learning-based interatomic potentials from ab initio electronic structure
methods remains a challenging task for computational chemistry and materials science. This …

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

Solving the Schr\" odinger equation is key to many quantum mechanical properties.
However, an analytical solution is only tractable for single-electron systems. Recently …

Deep learning has the potential to accelerate atomistic simulations, but existing models
suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert …

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 …

Recent neural network-based wave functions have achieved state-of-the-art accuracies in
modeling ab-initio ground-state potential energy surface. However, these networks can only …

Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …