用神经网络直接解薛定谔方程，预测二维材料磁性，无需额外物理假设。
Predicting magnetism with first-principles AI
- 用神经网络变分蒙特卡洛法求解多电子薛定谔方程，捕捉强关联效应。
- 在单次计算中预测出两种磁态：WSe2/WS2的巡游铁磁与Γ谷同质结的反铁磁绝缘体。
- 仅在Sz=0宇称下完成计算，大幅降低算力需求，适合高效设计新型磁性材料。
计算发现磁性材料仍具挑战性，因磁性源于动能与库仑相互作用的竞争，常超出标准电子结构方法的范围。本文通过神经网络变分蒙特卡洛法直接求解多电子薛定谔方程，为强关联体系提供高表达能力的变分波函数。将该方法应用于过渡金属二硫属化物莫尔半导体，预测了WSe₂/WS₂中的巡游铁磁态以及扭曲Γ谷同质结中的反铁磁绝缘态，且均使用同一神经网络，仅需微观哈密顿量作为输入，无额外物理假设。关键在于，两种磁态均在单次计算的S_z=0宇称子空间中获得，无需分别计算和比较多个S_z分支，显著降低计算成本，为更快速、可靠的磁性材料设计开辟新路径。
原文摘要 · Abstract (English)
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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