Pairing-based graph neural network for simulating quantum materials
This provides a scalable method for simulating quantum materials, addressing challenges in condensed matter physics, though it appears incremental as it builds on existing neural network wavefunction approaches.
The researchers tackled the simulation of quantum many-body systems by developing a pairing-based graph neural network that augments a BCS-type geminal wavefunction, achieving accurate results on interaction-induced phases like exciton Bose-Einstein condensates and electron-hole superconductors in two-dimensional semiconductor electron-hole bilayers.
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.