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arXiv · 2406.17645

Simulating moir\'e quantum matter with neural network

Di Luo·David D. Dai·Liang Fu
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Abstract

Moir\'e materials provide an ideal platform for exploring quantum phases of matter. However, solving the many-electron problem in moir\'e systems is challenging due to strong correlation effects. We introduce a powerful variational representation of quantum states, many-body neural Bloch wavefunction, to solve many-electron problems in moir\'e materials accurately and efficiently. Applying our method to the semiconductor heterobilayer WSe2/WS2 , we obtain a generalized Wigner crystal at filling factor n = 1/3, a Mott insulator n = 1, and a correlated insulator with local magnetic moments and antiferromagnetic spin correlation at n = 2. Our neural network approach improves the simulation accuracy of strongly interacting moir\'e materials and paves the way for discovery of new quantum phases with variational learning principle in a unified framework.

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Di Luo, David D. Dai, Liang Fu. 2024-06-25. Simulating moir\'e quantum matter with neural network. https://arxiv.org/abs/2406.17645

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