Predicting magnetism with first-principles AI

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 $\Gamma$-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.

Strongly Correlated Electrons (cond-mat.str-el); Machine Learning (cs.LG)

arXiv:2602.09093 [cond-mat.str-el]

(or arXiv:2602.09093v2 [cond-mat.str-el] for this version)

https://doi.org/10.48550/arXiv.2602.09093 Focus to learn more arXiv-issued DOI via DataCite

Phys. Rev. B (2026)

https://doi.org/10.1103/77s8-8s61 Focus to learn more DOI(s) linking to related resources

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