37 citations · 37 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
Superconductivity, Charge-Density-Waves, and Bipolarons in the Holstein model
B. Nosarzewski, E. W. Huang, Philip M. Dee +5
The electron-phonon (e-ph) interaction remains of great interest in condensed matter physics and plays a vital role in realizing superconductors, charge-density-waves (CDW), and po…
Accelerating lattice quantum Monte Carlo simulation using artificial neural networks: an application to the Holstein model
Shaozhi Li, Philip M. Dee, Ehsan Khatami +1
Monte Carlo (MC) simulations are essential computational approaches with widespread use throughout all areas of science. We present a method for accelerating lattice MC simulations…