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:创建快速和准确的力场是计算化学和材料科学的一个长期挑战。 最近,通过神经网络(MPNN)的一些等异信息显示在准确性方面优于使用其他方法建立的模式。 然而,大多数MPNN受到高计算成本和低缩性的影响。 我们提议这些限制的产生,因为MPNN仅传递双体信息,导致网络层数与表达性之间的直接关系。 在这项工作中,我们引入了MACE,这是一个使用较高体质顺序信息的新的等异MPNNN模型。特别是,我们显示使用四体信息将所需信息传递的重复数降低到仅两个,导致快速和高度平行的模型,达到或超过rMD17、3BPA和Ac基准任务的最新精确度。 我们还表明,使用更高的命令信息可以提高学习曲线的陡度。