Magnetic HIP-NN for spin dynamics in disordered itinerant magnets
We present a magnetic extension of the Hierarchically Interacting Particle Neural Network (HIP-NN) that enables large-scale simulations of electron-mediated spin dynamics in disordered itinerant magnets. The resulting magnetic HIP-NN (mHIP-NN) incorporates rotationally invariant spin correlations directly into hierarchical message-passing layers, enabling the network to learn emergent magnetic energy landscapes and effective local fields from coupled geometric-spin environments while preserving spin-rotation symmetry. As a benchmark application, we consider structurally disordered itinerant $s$-$d$ exchange models in which the effective magnetic forces arise dynamically from the instantan...
| Authors | Supriyo Ghosh, Yunhao Fan, Sheng Zhang et al. |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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