MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force FieldsDownload PDF

Published: 31 Oct 2022, Last Modified: 13 Jan 2023NeurIPS 2022 AcceptReaders: Everyone
Keywords: GNN, graph neural network, equivariance, higher order, message passing neural network, point clouds, molecules
Abstract: Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, Equivariant Message Passing Neural Networks (MPNNs) have emerged as a powerful tool for building machine learning interatomic potentials, outperforming other approaches in terms of accuracy. However, they suffer from high computational cost and poor scalability. Moreover, most MPNNs only pass two-body messages leading to an intricate relationship between the number of layers and the expressivity of the features. This work introduces MACE, a new equivariant MPNN model that uses higher order messages, and demonstrates that this leads to an improved learning law. We show that by using four-body messages, the required number of message passing iterations reduces to just one, resulting in a fast and highly parallelizable model, reaching or exceeding state of the art accuracy on the rMD17 and 3BPA benchmark tasks. Our implementation is available at
Supplementary Material: pdf
TL;DR: Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
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