Electric Polarization from a Many-Body Neural Network Ansatz

Xiang Li, Yubing Qian, and Ji Chen
Phys. Rev. Lett. 132, 176401 – Published 25 April 2024

Abstract

Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wave function ansatz combined with quantum Monte Carlo method to incorporate correlations into polarization calculations. On a variety of systems, including isolated atoms, one-dimensional chains, two-dimensional slabs, and three-dimensional cubes, the calculated results outperform conventional density functional theory and are consistent with the most accurate calculations and experimental data. Furthermore, we have studied the out-of-plane dielectric constant of bilayer graphene using our method and reestablished its thickness dependence. Overall, this approach provides a powerful tool to accurately describe electron correlation in the modern theory of polarization.

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  • Received 15 August 2023
  • Revised 1 December 2023
  • Accepted 22 March 2024

DOI:https://doi.org/10.1103/PhysRevLett.132.176401

© 2024 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Xiang Li1,*,†, Yubing Qian1,2,*, and Ji Chen2,3,‡

  • 1ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
  • 2School of Physics, Peking University, Beijing 100871, People’s Republic of China
  • 3Interdisciplinary Institute of Light-Element Quantum Materials, Frontiers Science Center for Nano-Optoelectronics, Peking University, Beijing 100871, People’s Republic of China

  • *X. L. and Y. Q. contributed equally to this work.
  • lixiang.62770689@bytedance.com
  • ji.chen@pku.edu.cn

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Issue

Vol. 132, Iss. 17 — 26 April 2024

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