SciPost Physics

SciPost Phys. 14, 154 (2023) · published 14 June 2023

• doi: 10.21468/SciPostPhys.14.6.154
• pdf
• BiBTeX
• RIS
• Submissions/Reports
• 

Abstract

The quasiparticle effective mass $m^\ast$ of interacting electrons is a fundamental quantity in the Fermi liquid theory. However, the precise value of the effective mass of uniform electron gas is still elusive after decades of research. The newly developed neural canonical transformation approach [Xie et al., J. Mach. Learn. 1, (2022)] offers a principled way to extract the effective mass of electron gas by directly calculating the thermal entropy at low temperature. The approach models a variational many-electron density matrix using two generative neural networks: an autoregressive model for momentum occupation and a normalizing flow for electron coordinates. Our calculation reveals a suppression of effective mass in the two-dimensional spin-polarized electron gas, which is more pronounced than previous reports in the low-density strong-coupling region. This prediction calls for verification in two-dimensional electron gas experiments.

• Humeniuk et al., Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
  SciPost Phys. 14, 171 (2023) [Crossref]
• Holzmann et al., Static Self-Energy and Effective Mass of the Homogeneous Electron Gas from Quantum Monte Carlo Calculations
  Phys. Rev. Lett. 131, 186501 (2023) [Crossref]
• Xie et al., Deep Variational Free Energy Approach to Dense Hydrogen
  Phys. Rev. Lett. 131, 126501 (2023) [Crossref]
• Zhao 赵 et al., Bounding Free Energy Difference with Flow Matching
  Chinese Phys. Lett. 40, 120201 (2023) [Crossref]