16 citations · 54 across the 8 of their papers we have counts for
9 papers
Probing Collective and Individual Kondo Screening: Multi-Stage, Multi-Channel Kondo Effects in a -Symmetric Four-Impurity Model
Danqing Hu, Jiangfan Wang, Zixiang Hu +1
Collective screening has been proposed to underlie the basic physics of multi-impurity and lattice Kondo systems. But how to establish this picture and distinguish it from individu…
Generalized Spectral Decomposition for Quantum Impurity Problems
Jun-Bin Wang, Dongchen Huang, Yi-feng Yang
Solving quantum impurity problems may advance our understanding of strongly correlated electron physics, but its development in multi-impurity systems has been greatly hindered due…
Auxiliary-Bath Numerical Renormalization Group Method and Successive Collective Screening in Multi-Impurity Kondo Systems
Danqing Hu, Jiangfan Wang, Yi-feng Yang
We propose an auxiliary-bath algorithm for the numerical renormalization group (NRG) method to solve multi-impurity models with shared electron baths. The method allows us to disen…
Nonlocal Kondo effect and two-fluid picture revealed in an exactly solvable model
Jiangfan Wang, Yi-feng Yang
Understanding the nature of local-itinerant transition of strongly correlated electrons is one of the central problems in condensed matter physics. Heavy fermion systems describe t…
Superconducting fluctuations and charge-4 plaquette state at strong coupling
Qiong Qin, Jian-Jun Dong, Yutao Sheng +2
We apply the static auxiliary field Monte Carlo approach to study phase correlations of the pairing fields in a microscopic model with spin-singlet pairing interaction. We find tha…
Removing grid structure in angle-resolved photoemission spectra via deep learning method
Junde Liu, Dongchen Huang, Yi-feng Yang +1
Spectroscopic data may often contain unwanted extrinsic signals. For example, in ARPES experiment, a wire mesh is typically placed in front of the CCD to block stray photo-electron…
Spectroscopic data de-noising via training-set-free deep learning method
Dongchen Huang, Junde Liu, Tian Qian +1
De-noising plays a crucial role in the post-processing of spectra. Machine learning-based methods show good performance in extracting intrinsic information from noisy data, but oft…
Learning disentangled representation for classical models
Dongchen Huang, Danqing Hu, Yi-feng Yang
Finding disentangled representation plays a predominant role in the success of modern deep learning applications, but the results lack a straightforward explanation. Here we apply…
Learned Optimizers for Analytic Continuation
Dongchen Huang, Yi-feng Yang
Traditional maximum entropy and sparsity-based algorithms for analytic continuation often suffer from the ill-posed kernel matrix or demand tremendous computation time for paramete…