2 papers
cond-mat.str-el2026
Neural networks as low-cost surrogates for impurity solvers in quantum embedding methods
Rohan Nain, Philip M. Dee, Kipton Barros +2
A promising application of machine learning is the creation of low-cost surrogate models to mitigate computational bottlenecks in quantum many-body simulations. Here, we explore wh…
cond-mat.supr-con2025
Optimizing the Critical Temperature and Superfluid Density of a Metal-Superconductor Bilayer
Yutan Zhang, Philip M. Dee, Benjamin Cohen-Stead +3
A promising path to realizing higher superconducting transition temperatures is the strategic engineering of artificial heterostructures. For example, quantum materials could…