Pairing-based graph neural network for simulating quantum materials

Di Luo; David D. Dai; Liang Fu

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arXiv · 2311.02143

Pairing-based graph neural network for simulating quantum materials

Di Luo·David D. Dai·Liang Fu

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Abstract

We develop a pairing-based graph neural network for simulating quantum many-body systems. Our architecture augments a BCS-type geminal wavefunction with a generalized pair amplitude parameterized by a graph neural network. Variational Monte Carlo with our neural network simultaneously provides an accurate, flexible, and scalable method for simulating many-electron systems. We apply this method to two-dimensional semiconductor electron-hole bilayers and obtain accurate results on a variety of interaction-induced phases, including the exciton Bose-Einstein condensate, electron-hole superconductor, and bilayer Wigner crystal. Our study demonstrates the potential of physically-motivated neural network wavefunctions for quantum materials simulations.

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cond-mat.str-el cond-mat.dis-nn cs.LG physics.comp-ph quant-ph

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BibTeX RIS

Di Luo, David D. Dai, Liang Fu. 2023-11-03. Pairing-based graph neural network for simulating quantum materials. https://arxiv.org/abs/2311.02143

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