# Predicting magnetism with first-principles AI

arXiv: https://arxiv.org/abs/2602.09093 · HTML: https://mlsanity.com/paper/2602.09093/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 77 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [Optimizing Neural Networks with Kronecker-factored Approximate Curvature](https://mlsanity.com/paper/1503.05671/) (2015)
- [Are Transformers universal approximators of sequence-to-sequence functions?](https://mlsanity.com/paper/1912.10077/) (2019)
- [Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networks](https://mlsanity.com/paper/1909.02487/) (2019)
- [A Self-Attention Ansatz for Ab-initio Quantum Chemistry](https://mlsanity.com/paper/2211.13672/) (2022)
- [Discovering Quantum Phase Transitions with Fermionic Neural Networks](https://mlsanity.com/paper/2202.05183/) (2022)
- [A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions](https://mlsanity.com/paper/2401.10190/) (2024)
- [Transferable Neural Wavefunctions for Solids](https://mlsanity.com/paper/2405.07599/) (2024)
- [First-Principles AI finds crystallization of fractional quantum Hall liquids](https://mlsanity.com/paper/2602.03927/) (2026)

## Abstract (arXiv)

Computational discovery of magnetic materials remains challenging because magnetism arises from the competition between kinetic energy and Coulomb interaction that is often beyond the reach of standard electronic-structure methods. Here we tackle this challenge by directly solving the many-electron Schr"odinger equation with neural-network variational Monte Carlo, which provides a highly expressive variational wavefunction for strongly correlated systems. Applying this technique to transition metal dichalcogenide moir'e semicondutors, we predict itinerant ferromagnetism in WSe2/WS2 and an antiferromagnetic insulator in twisted Gamma-valley homobilayer, using the same neural network without any physics input beyond the microscopic Hamiltonian. Crucially, both types of magnetic states are obtained from a single calculation within the Sz=0 sector, removing the need to compute and compare multiple Sz sectors. This significantly reduces computational cost and paves the way for faster and more reliable magnetic material design.