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arXiv Machine Learning By Erh-Wei Sheng, Beom Seok Kang, Yuchao Lin, Danish Khan, Anima Anandkumar

SU(2)-Equivariant Spinor Message Passing for Molecular Magnetism

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The paper introduces eSpinor, an SU(2)-equivariant graph neural network that propagates spinor features for predicting molecular magnetism. By initializing spinor features from eigenvectors of an approximate spin‑orbit coupling Hamiltonian and employing gauge‑covariant message passing with a gauge‑invariant readout, eSpinor achieves more accurate zero‑field‑splitting predictions for cobalt single‑molecule magnets and improves electron‑spin correlation predictions for a dysprosium MRI probe compared to existing methods. The study demonstrates that incorporating SU(2) symmetry as an inductive bias enhances performance on magnetic properties and highlights that static tensor errors can lead to different dynamical errors, suggesting careful model selection for specific observables.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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