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 a neural-network variational Monte Carlo technique, which provides a highly expressive variational wave function for strongly correlated systems. Applying this technique to transition metal dichalcogenide moiré semicondutors, we predict itinerant ferromagnetism in ${\mathrm{WSe}}_2$/${\mathrm{WS}}_2$ and an antiferromagnetic insulator in twisted $\mathrm{\Gamma }$-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 $S_z=0$ 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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• Magnetism
• Moire materials
• Monte Carlo methods
• Neural network simulations
• Variational wave functional methods