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.

翻译：构建快速精确的力场是计算化学与材料科学领域的长期挑战。近期，多种等变消息传递神经网络（MPNNs）在精度上已被证明优于采用其他方法构建的模型。然而，大多数MPNNs存在计算成本高、可扩展性差的问题。我们提出，这些局限性源于MPNNs仅传递二体消息，导致网络层数与表达能力存在直接关联。本研究提出MACE——一种使用高阶体消息的新型等变MPNN模型。特别地，我们证明采用四体消息可将所需的消息传递迭代次数降至仅两次，从而构建出快速且高度可并行化的模型，在rMD17、3BPA和AcAc基准任务上达到或超越当前最优精度。我们还证实，使用高阶消息能显著提升学习曲线的陡峭程度。