  This paper introduces E(n) Equivariant Message Passing Cellular Networks
(EMPCNs), an extension of E(n) Equivariant Graph Neural Networks to
CW-complexes. Our approach addresses two aspects of geometric message passing
networks: 1) enhancing their expressiveness by incorporating arbitrary cells,
and 2) achieving this in a computationally efficient way with a decoupled
EMPCNs technique. We demonstrate that EMPCNs achieve close to state-of-the-art
performance on multiple tasks without the need for steerability, including
many-body predictions and motion capture. Moreover, ablation studies confirm
that decoupled EMPCNs exhibit stronger generalization capabilities than their
non-topologically informed counterparts. These findings show that EMPCNs can be
used as a scalable and expressive framework for higher-order message passing in
geometric and topological graphs
