Predicting magnetism with first-principles AI

arXiv:2602.09093 · cond-mat.str-el, cs.LG · Submitted 2026-02-09 · Read on arXiv

cond-mat.str-el, cs.LG

Submitted: 2026-02-09

Updated: 2026-09-21

Comments: 6+3 pages, 3+4 figures

Journal ref: Phys. Rev. B (2026)

DOI: 10.1103/77s8-8s61

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Hubbard Model — 6× in this paper · explained in 3 episodes
• Antiferromagnetism — 4× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Ab Initio — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Antiferromagnetic Ordering — 1× in this paper · explained in 1 episode
• Chern Number — 1× in this paper · explained in 4 episodes
• DFT Calculations — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Topological Order — 1× in this paper · explained in 2 episodes

Sources

• Metallic Ferromagnetism - an Electronic Correlation Phenomenon
• Attention is all you need to solve chiral superconductivity
• Electronic crystals and quasicrystals in semiconductor quantum wells: an AI-powered discovery
• Topological Order in Neural Wavefunctions
• First-Principles AI finds crystallization of fractional quantum Hall liquids
• A minimal and universal representation of fermionic wavefunctions (fermions = bosons + one)
• Fermi Sets: Universal and interpretable neural architectures for fermions
• Neural Wave Functions for High-Pressure Atomic Hydrogen
• Deep Learning Sheds Light on Integer and Fractional Topological Insulators
• Solving fractional electron states in twisted MoTe$_2$ with deep neural network
• Extracting Anyon Statistics from Neural Network Fractional Quantum Hall States
• Transferable Neural Wavefunctions for Solids
• Expressivity of determinantal ansatzes for neural network wave functions
• Density functional approach to correlated moire states: itinerant magnetism

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• Instabilities in self-consistent diagrammatic approaches and how to cure them
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