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[Submitted on 15 Jun 2022 (v1), last revised 26 Jan 2023 (this version, v2)]

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

Authors:Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, Gábor Csányi
View a PDF of the paper titled MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields, by Ilyes Batatia and 4 other authors
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Abstract: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.
Comments: Advances in Neural Information Processing Systems, 2022
Subjects: Machine Learning (stat.ML); Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
Cite as: arXiv:2206.07697 [stat.ML]
  (or arXiv:2206.07697v2 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2206.07697
arXiv-issued DOI via DataCite

Submission history

From: Ilyes Batatia [view email]
[v1] Wed, 15 Jun 2022 17:46:05 UTC (268 KB)
[v2] Thu, 26 Jan 2023 10:07:20 UTC (367 KB)
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