9 citations · 10 across the 7 of their papers we have counts for
7 papers
Hybrid Hamiltonian-diagrammatic quantum impurity solver
Yang Yu, Gaurav Harsha, Lei Zhang +4
Quantum impurity models, which describe the coupling between interacting orbitals and a non-interacting bath, play a central role in the physics of strongly correlated electron sys…
Evolution of the Saddle Point in Antimony Telluride Homologous Superlattices
Yi-Hsin Shen, Shane Smolenski, Ming Wen +13
Combining topological insulators with topological semimetals in the form of homologous superlattices is a promising approach for generating correlated quantum matter based upon Fer…
H-NESSi: The Hierarchical Non-Equilibrium Systems Simulation package
Thomas Blommel, Jeremija Kovačević, Jason Kaye +3
We present H-NESSi (The Hierarchical Non-Equilibrium Systems Simulation package), an open-source software package for solving the Kadanoff-Baym equations (KBE) of nonequilibrium Gr…
Multi-orbital dynamical mean-field theory with a complex-time solver
Yang Yu, Lei Zhang, Emanuel Gull +2
We present the combination of a complex-time tensor-network impurity solver with an analytic continuation scheme based on exponential fitting as an efficient framework for single a…
Symmetry-resolved topology from interacting Green's functions
Gaurav Harsha, Selina Dirnböck, Emanuel Gull +3
Topological phases in narrow-gap materials often arise from a delicate interplay of spin-orbit coupling (SOC) and electron correlation, making their prediction sensitive to the und…
Symmetry adaptation for self-consistent many-body calculations
Xinyang Dong, Emanuel Gull
The exploitation of space group symmetries in numerical calculations of periodic crystalline solids accelerates calculations and provides physical insight. We present results for a…
Equivariant neural network for Green's functions of molecules and materials
Xinyang Dong, Emanuel Gull, Lei Wang
The many-body Green's function provides access to electronic properties beyond density functional theory level in ab inito calculations. In this manuscript, we propose a deep learn…