Impact of Electron-Phonon Coupling on Graphene Intercalation Compounds from Self Energy: Polynomial Models Selection

Abstract

In our pursuit of understanding electron-phonon coupling (EPC) and their impact on material properties, we delved deep into the intricate role played by the Eliashberg function in governing electron self-energy. Through meticulous evaluation of tailored polynomial models approximating this function, we unearthed profound insights into how phonon interactions intricately modify electronic energy bands. Employing numerical computations, we meticulously unraveled both the real and imaginary aspects of electron self-energy, crucial in comprehending EPC effects in various materials. Investigating superconductivity within monolayer graphene and its interaction with diverse doping substances, our study led us to identify optimal polynomial models that accurately capture EPC behaviors, offering invaluable implications for predicting critical temperatures in superconducting materials. Expanding the parameters within our models allowed us to anticipate changes in self-energy models for higher-order configurations not explored in this study. Our selection of polynomial spanning degrees from n = 1 to 10 the efficacy of n = 2 (Debye) as the most realistic and accurate model, closely followed by n = 1, albeit occasional deviations observed in specific materials. These discrepancies often stemmed from noise model inaccuracies and parameter approximations. Our comprehensive approach outshone the traditional Kramer-Kronig transform in assessing electron-phonon interactions. Looking ahead, the application of multiple models to the Eliashberg function diagram holds immense promise for enhancing accuracy, despite the challenge of concurrently adjusting multiple input parameters. This integration of numerical modeling with experimental data forms a robust framework, empowering the prediction and fine-tuning of material properties vital for the future fabrication of devices.

HIGHLIGHTS

• Exploring the use of polynomial models to approximate the Eliashberg function, enabling a detailed analysis of electron-phonon coupling (EPC) in materials.
• Confirm the Debye model's accuracy in predicting the electronic structure of doped graphene.
• The findings suggest enhanced EPC in materials like CaC6 and its potential to induce superconductivity in monolayer graphene.
• This work offers a framework for future research and applications in superconducting materials using ARPES data.

GRAPHICAL ABSTRACT