Predicting magnetism with first-principles AI

• URL: http://arxiv.org/abs/2602.09093v1
• Date: Mon, 09 Feb 2026 19:00:01 GMT
• Title: Predicting magnetism with first-principles AI
• Authors: Max Geier, Liang Fu,
• Abstract summary: We solve the many-electron Schrdinger equation with neural-network variational Monte Carlo.<n>We predict itinerantmagnetism in WSe$$/WS$magnetic$ and an antiferro insulator in twisted $$-valley homobilayer.<n>This significantly reduces computational cost and paves the way for faster and more reliable magnetic material design.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Schrödinger 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 moiré semicondutors, we predict itinerant ferromagnetism in WSe$_2$/WS$_2$ and an antiferromagnetic insulator in twisted $Γ$-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 $S_z=0$ sector, removing the need to compute and compare multiple $S_z$ sectors. This significantly reduces computational cost and paves the way for faster and more reliable magnetic material design.

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