58 citations · 203 across the 21 of their papers we have counts for
6 papers · 1 filter
Strain-driven orbital-selective reconstruction and bicollinear-to-stripe evolution in FeTe
Zhenfeng Ouyang, Yin Chen, Yi-Heng Tian +4
FeTe, as a representative parent material among iron-based superconductors, provides an ideal platform for exploring the interplay among orbital-selective correlations, magnetism,…
Local Spin Excitations Mediate Quasiparticle Breakdown in the Orbital-Selective Mott Phase
Yuekun Niu, Yu Ni, Jia-Ming Wang +3
The orbital-selective Mott phase (OSMP) is commonly described as a coexistence of localized and itinerant electrons within effectively decoupled orbitals, but emerging evidence for…
Optimization dynamics of Transformer backflow neural quantum states for the two-dimensional Hubbard model
Zong-Yu Liao, Jia-Qi Wang, Rong-Qiang He +1
Building on the multi-determinant Transformer backflow neural quantum state (NQS) ansatz and the associated multi-stage training workflow for the doped two-dimensional Hubbard mode…
: An open-source quantum impurity solver package based on the natural orbitals renormalization group
Jia-Ming Wang, Yi-Heng Tian, Yin Chen +3
In the context of dynamical mean-field theory (DMFT) calculations for strongly correlated electron systems, quantum impurity solvers play a central computational role in treating c…
Layer-resolved Electronic Structure and Correlation of Low- Square-planar Nickelates: A DFT+DMFT Prediction of Superconducting Candidates
Jian-Hong She, Rong-Qiang He, Zhong-Yi Lu
Multi-layer square-planar nickelates provide a rare platform in which the nominal Ni valence, dimensionality, and layer-resolved electronic structure can be tuned within the same s…
Electronic structure and correlation of LaCoNiOCl: a theoretical proposal for a LaNiO-like high-temperature superconductor
Si-Yong Jia, Jing-Xuan Wang, Jian-Hong She +2
Based on the discovery of high-temperature superconductivity in the bilayer nickelate LaNiO, several Co-based LaNiO-like materials were theoretically predic…