Understanding interaction-driven superconductivity with neural network wavefunctions

Abstract:

Electron-electron interactions are believed to play a crucial role in stabilizing a wide variety of unconventional superconductors, including the high-temperature cuprates and nickelates, iron pnictides, and recently discovered moiré materials. However, understanding these systems beyond perturbatively controlled regimes remains a formidable challenge due to the exponential complexity of the many-electron Schrödinger equation. In recent years, neural network wavefunctions have emerged as a powerful numerical tool for studying strongly correlated electrons. 

In this talk, I will present results from a recent numerical study that shows robust superconductivity emerges upon doping chiral quantum phases. We find that doping near the Mott transition produces a superconductor with anomalously large phase stiffness, suggesting that frustration-driven charge fluctuations may provide a mechanism for enhancing superconductivity. I will conclude by discussing the broader outlook for neural network wavefunctions and their potential to uncover new organizing principles of strongly correlated quantum matter.