<ul data-eligibleForWebStory="true"><li>Researchers have introduced an efficient network, QHNetV2, for predicting Hamiltonian matrices to speed up electronic structure calculations.</li><li>The network achieves global SO(3) equivariance without using costly SO(3) Clebsch-Gordan tensor products.</li><li>The approach is based on the relationship between off-diagonal blocks of the Hamiltonian matrix and the SO(2) local frame.</li><li>New efficient and powerful SO(2)-equivariant operations are introduced to perform all off-diagonal feature updates and message passing within SO(2) local frames.</li><li>Continuous SO(2) tensor product is executed within the SO(2) local frame at each node to fuse node features.</li><li>Experiments on QH9 and MD17 datasets exhibit the model's high performance across various molecular structures and trajectories.</li><li>The proposed SO(2) operations offer a scalable and symmetry-aware approach for learning electronic structures.</li><li>The code will be accessible as part of the AIRS library on GitHub at https://github.com/divelab/AIRS.</li></ul>

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames

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