Measuring trainable degrees of freedom in materials graph neural networks: a random-subspace intrinsic dimension analysis
cond-mat.mtrl-sci, cs.AI
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Related papers
- Mode-selective electron-phonon coupling drives charge density waves in the kagome metals YRu 3 Si 2 and LaRu 3 Si 2
- Nonlinear dynamics in magnonic Fabry-P' e rot resonators: Low-power neuron-like activation and transmission suppression
- AES-Debye: an Accurate, Efficient, and Scalable Engine for Debye Scattering Calculations
- Cooperative Quantum Optical Effects of Moir'e Exciton Superlattices
- Imaging Surface Magnetization in Altermagnetic MnTe Films
- Accidental accuracy and formal consistency in GW +BSE: Exact benchmarks and regime-dependent error cancellation