eSpinor: Anima Anandkumar's team proposes SU(2)-equivariant GNN for magnetic properties
AnimaAnandkumar · x · 2026-10-31
Inspired by the spinor nature of spin–orbit-coupled electronic states, Anima Anandkumar's Caltech team introduces eSpinor, an SU(2)-equivariant graph neural network that propagates spinor features to predict spin-orbit coupling (SOC)-dependent magnetic properties.
Background:
- Properties like magnetic anisotropy, excitation energies and spin dynamics guide the design of single-molecule magnets, MRI probes and other spin-based technologies.
- Predicting them typically requires CASSCF-SO multireference calculations, which can take hours for a single molecular geometry.
Core idea:
- SO(3)-equivariance has already improved rotation-invariant energy prediction in machine-learning interatomic potentials; the team goes further by imposing SU(2)-equivariance on spinor features as an inductive bias for magnetic property prediction.
More from Research
- GOFish: fisheye-trained 3D Gaussian Splatting surpasses 180° FoV and extracts collision-ready meshes — janusch_patas · 2026-10-31
- Artifact Distillation: training-free transfer lets Claude Haiku 4.5 beat Sonnet 4.5 at 60% cost — kalyan_kpl · 2026-10-31
- AI researcher argues 90% of research is too vague to be wrong as automated review costs drop to zero — RexDouglass · 2026-10-31
- Microsoft's ScholarEvolve evolves agent harnesses with frozen models, +14 points on AppWorld — jiqizhixin · 2026-10-31
- Exponential Residual Flows: an ODE sampler for discrete diffusion models — _onionesque · 2026-10-31
- Robots lay 100% of bricks for housing project in Almere, 15 more homes coming — ZeYanjie · 2026-10-31