Pairing-based graph neural network for simulating quantum materials

• URL: http://arxiv.org/abs/2311.02143v2
• Date: Tue, 21 Nov 2023 15:54:28 GMT
• Title: Pairing-based graph neural network for simulating quantum materials
• Authors: Di Luo, David D. Dai, and Liang Fu
• Abstract summary: We develop a pairing-based graph neural network for simulating quantum many-body systems. Variational Monte Carlo with our neural network simultaneously provides an accurate, flexible, and scalable method for simulating many-electron systems.
• Score: 0.8192907805418583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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