  In this paper, we develop SE3Set, an SE(3) equivariant hypergraph neural
network architecture tailored for advanced molecular representation learning.
Hypergraphs are not merely an extension of traditional graphs; they are pivotal
for modeling high-order relationships, a capability that conventional
equivariant graph-based methods lack due to their inherent limitations in
representing intricate many-body interactions. To achieve this, we first
construct hypergraphs via proposing a new fragmentation method that considers
both chemical and three-dimensional spatial information of molecular system. We
then design SE3Set, which incorporates equivariance into the hypergragh neural
network. This ensures that the learned molecular representations are invariant
to spatial transformations, thereby providing robustness essential for accurate
prediction of molecular properties. SE3Set has shown performance on par with
state-of-the-art (SOTA) models for small molecule datasets like QM9 and MD17.
It excels on the MD22 dataset, achieving a notable improvement of approximately
20% in accuracy across all molecules, which highlights the prevalence of
complex many-body interactions in larger molecules. This exceptional
performance of SE3Set across diverse molecular structures underscores its
transformative potential in computational chemistry, offering a route to more
accurate and physically nuanced modeling.
