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Accurate Electron-Phonon Interactions from Advanced Density Functional Theory

Yanyong Wang1,*, Manuel Engel2,*, Christopher Lane3, Yubo Zhang4, Henrique Miranda2, Lin Hou1,3, Bernardo Barbiellini5,6,7, Robert S. Markiewicz6,7, Jian-Xin Zhu3 et al.

Georg Kresse2,8, Arun Bansil6,7,†, Jianwei Sun1,‡, and Ruiqi Zhang1,*,§

  • *These authors contributed equally to this work.
  • †Contact author: ar.bansil@neu.edu
  • ‡Contact author: jsun@tulane.edu
  • §Contact author: rzhang16@tulane.edu

PRX Energy 5, 013002 – Published 13 January, 2026

DOI: https://doi.org/10.1103/w56r-f5yy

Abstract

Electron-phonon coupling (EPC) is key to understanding charge transport, band renormalization, and superconductivity in energy materials, including correlated transition-metal oxides, ferroelectric perovskites, optoelectronic semiconductors, and phonon-mediated superconductors. Although first-principles density functional theory (DFT)-based EPC calculations are used widely, their predictive power is limited by the accuracy, transferability, and efficiency of the underlying exchange-correlation functionals. These limitations become exacerbated in complex d- and f-electron materials, where beyond-DFT approaches and additional corrections, such as the Hubbard U, are commonly invoked. Here, using the examples of CoO and NiO, we show how the r2SCAN density functional correctly captures strong EPC effects in transition-metal oxides without requiring the introduction of Hubbard U parameters. We also find that r2SCAN successfully describes the subtle interplay between ionic and covalent bonding, and strong EPC effects in the low-temperature rhombohedral phase of the prototypical ferroelectric BaTiO3, without requiring Hubbard U and intersite V corrections. We further demonstrate the ability of r2SCAN to accurately model the EPC of the main-group semiconductor GaAs and the phonon-mediated superconducting properties of MgB2, with reliable electronic bands and phonons. Our study establishes r2SCAN-based EPC as a transferable, parameter-free, and computationally efficient framework for predictive material-specific modeling of EPC in energy materials, and opens a practical route toward high-throughput screening of superconductors, thermoelectrics, optoelectronic semiconductors, and oxide electrodes and catalysts.

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