Low-Scaling Many-Body Green's Function Calculations for Molecular Systems via Interacting-Bath Dynamical Embedding Theory

TL;DR

This paper introduces an efficient embedding method for calculating molecular spectral properties, significantly reducing computational cost while maintaining high accuracy in ionization potentials and electron affinities.

Contribution

The authors develop an interacting-bath dynamical embedding theory (ibDET) that enables scalable and accurate Green's function calculations for molecular systems.

Findings

ibDET achieves errors around 0.1 eV in ionization potentials and electron affinities.

The method reduces computational cost by focusing on small embedding problems.

Accurate spectral properties are obtained for conjugated molecules and nanoclusters.

Abstract

We present a molecular extension of our recently proposed Green's function embedding method, interacting-bath dynamical embedding theory (ibDET), for computing charged excitation energies at the $GW$ and EOM-CCSD levels. Starting from atom-centered impurities, we construct bath representations that capture the frequency-dependent entanglement between the impurity and its environment and can be systematically improved via the construction of cluster-specific natural orbitals. Utilizing a $GW$ or coupled-cluster Green's function solver, the self-energy of the full system is assembled from all embedding problems to obtain the interacting Green's function. We show that ibDET provides accurate spectral properties with much reduced cost for a broad range of systems, including conjugated molecules and nanoclusters. Compared with full-system results, the errors in the predicted ionization potentials and electron affinities are around 0.1 eV or smaller, while each embedding problem includes only a small fraction of the total orbital space. This work provides an efficient and scalable framework for computing spectral properties of molecular systems.