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Creating fast and accurate force fields is a long-standing challenge incomputational chemistry and materials science. Recently, several equivariantmessage passing neural networks (MPNNs) have been shown to outperform modelsbuilt using other approaches in terms of accuracy. However, most MPNNs sufferfrom high computational cost and poor scalability. We propose that theselimitations arise because MPNNs only pass two-body messages leading to a directrelationship between the number of layers and the expressivity of the network.In this work, we introduce MACE, a new equivariant MPNN model that uses higherbody order messages. In particular, we show that using four-body messagesreduces the required number of message passing iterations to just two,resulting in a fast and highly parallelizable model, reaching or exceedingstate-of-the-art accuracy on the rMD17, 3BPA, and AcAc benchmark tasks. We alsodemonstrate that using higher order messages leads to an improved steepness ofthe learning curves.

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