activity
20222026
most citedMachine learning assisted derivation of effective low energy models for metallic magnets

11 citations · 13 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.str-el2026

Semiclassical representation of the Hubbard model

Yuki Yamasaki, Hidemaro Suwa, Cristian D. Batista +1

By revisiting the path-integral formulation of the Hubbard model, we propose a theoretical approach based on a semiclassical approximation employing an unconventional coherent-stat…

cond-mat.str-el2026

Superconductivity and fractionalized magnetic excitations in CeCoIn5

Pyeongjae Park, Shang-Shun Zhang, Pietro M. Bonetti +8

Recent experiments on CeCoIn5 -- a prototypical d-wave superconductor -- indicate that its normal state lies near an unconventional quantum critical point (QCP). One intriguing hyp…

quant-ph2025★ 2 cited

Sunny.jl: A Julia Package for Spin Dynamics

David Dahlbom, Hao Zhang, Cole Miles +16

Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of n…

cond-mat.str-el2024

Derivation of low-energy Hamiltonians for heavy-fermion Materials

E. A. Ghioldi, Zhentao Wang, L. M. Chinellato +7

By utilizing a multi-orbital periodic Anderson model with parameters obtained from \textit{ab initio} band structure calculations, combined with degenerate perturbation theory, we…

cond-mat.str-el2022★ 11 cited

Machine learning assisted derivation of effective low energy models for metallic magnets

Vikram Sharma, Zhentao Wang, Cristian D. Batista

We consider the problem of extracting an effective low-energy spin model from a Kondo Lattice Model (KLM) with classical localized moments. The non-analytic dependence of the effec…