Beijing AI for Science Institute

Abstract:

The mechanism of electronic superconductivity in systems with intertwined orders has been debated for a long time. Several electronic mechanisms have been proposed, but so far there is no unbiased way of attributing superconductivity to any one of them. In this talk, I will present numerical results that quantitatively analyze the nature of superconductivity in a non-perturbative calculation of the two-dimensional Hubbard model within the dynamical cluster approximation. The simulation results show that at intermediate-to-strong interaction strengths, the one-loop spin fluctuation theory can not fully explain the pairing of electrons in the singlet superconducting state. To have a generalized and unbiased way of analyzing all possible fluctuations on equal footing, we generalized the fluctuation diagnostics method to the ordered superconducting state. By analyzing the anomalous self-energy in the superconducting state, our fluctuation diagnostics calculation precisely identifies antiferromagnetic spin fluctuations as the glue of the d−wave pairing.

Location: M830

Bio:

Xinyang Dong received her bachelor's degree from Peking University, Yuanpei college in 2017 (physics major). After that, she received her Ph.D. in physics and scientific computing from University of Michigan, Ann Arbor in 2022. Her Ph.D. research focused on the numerical methods for two-particle fluctuations and real-time dynamics of strongly correlated electron systems, especially understanding the mechanism of superconductivity in the Hubbard model from two-particle perspective. She is a researcher at the AI for Science Institute, Beijing currently, working in the interdisciplinary field of machine learning and many-body physics.

Contact: Lei Wang 82649853