First-principles modeling of plasmons in aluminum under ambient and extreme conditions

Kushal Ramakrishna, Attila Cangi, Tobias Dornheim, Andrew Baczewski, and Jan Vorberger
Phys. Rev. B 103, 125118 – Published 8 March 2021

Abstract

The theoretical understanding of plasmon behavior is crucial for an accurate interpretation of inelastic scattering diagnostics in many experiments. We highlight the utility of linear response time-dependent density functional theory (LR-TDDFT) as a first-principles framework for consistently modeling plasmon properties. We provide a comprehensive analysis of plasmons in aluminum from ambient to warm dense matter conditions and assess typical properties such as the dynamical structure factor, the plasmon dispersion, and the plasmon lifetime. We compare our results with scattering measurements and with other TDDFT results as well as models such as the random phase approximation, the Mermin approach, and the dielectric function obtained using static local field corrections of the uniform electron gas parametrized from path-integral Monte Carlo simulations. We conclude that results for the plasmon dispersion and lifetime are inconsistent between experiment and theories and that the common practice of extracting and studying plasmon dispersion relations is an insufficient procedure to capture the complicated physics contained in the dynamic structure factor in its full breadth.

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  • Received 28 September 2020
  • Revised 25 January 2021
  • Accepted 15 February 2021

DOI:https://doi.org/10.1103/PhysRevB.103.125118

©2021 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsPlasma Physics

Authors & Affiliations

Kushal Ramakrishna1,2,3,*, Attila Cangi3, Tobias Dornheim3, Andrew Baczewski4, and Jan Vorberger1

  • 1Helmholtz-Zentrum Dresden-Rossendorf (HZDR), D-01328 Dresden, Germany
  • 2Technische Universität Dresden, D-01062 Dresden, Germany
  • 3Center for Advanced Systems Understanding (CASUS), D-02826 Görlitz, Germany
  • 4Center for Computing Research, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA

  • *k.ramakrishna@hzdr.de

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Issue

Vol. 103, Iss. 12 — 15 March 2021

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