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