Many-Body Perturbation Theory with Generalized Poisson Equation for Solid-Liquid Interfaces

Understanding the electronic and quasiparticle structures of molecules and materials in solution is crucial for revealing the complex phenomena that occur at solid-liquid interfaces. However, traditional single scale computational methods, such as density functional theory, molecular dynamics, or continuum solvation models, often fall short in accurately describing solvated systems due to limitations in spatial and temporal resolution or in electronic structure predictions. To overcome these challenges, a seamless multiscale framework that integrates different theoretical approaches without artificial boundaries is needed, motivating the development of embedding methods.

In this work, we present an embedded many-body perturbation theory based on the generalized Poisson equation (GPE). This approach, referred to as GW-GPE, combines the GW approximation with an implicit solvation model that solves the GPE. In this framework, the GW approximation captures the accurate electronic structure of the solute or solid material, while the GPE effectively represents the surrounding solvent or electrolyte environment.

Using the GW-GPE method, we explored the electronic structures, including band edge positions, of quasi two-dimensional systems immersed in water. The calculated results show excellent agreement with experimental observations. Our study reveals two key solvent induced effects, the polarization field effect and the environmental screening effect, both of which are highly sensitive to the atomic and charge distributions within the materials. Furthermore, the method uncovers structure property relationships in porous systems, demonstrating its broad applicability.

Overall, the GW-GPE framework provides a powerful and accurate tool for predicting solvated electronic structures, including HOMO and LUMO levels, in low dimensional and interfacial systems. In this presentation, I will discuss the theoretical foundations of the method and highlight results that show strong consistency with experimental data.