Scalable and predictive spectra of correlated molecules with moment truncated iterated perturbation theory

TL;DR

This paper presents a scalable, self-consistent many-body perturbation theory method that accurately predicts the spectra of correlated molecules, improving upon standard approaches and enabling applications to larger systems.

Contribution

It introduces a novel, low-scaling, self-consistent perturbation theory approach with moment truncation for accurate spectral predictions in correlated molecules.

Findings

Accurately predicts spectra of correlated molecules.

Capable of handling systems with up to ~1000 orbitals.

Resolves previous discrepancies in the spectral analysis of artemisinin.

Abstract

A reliable and efficient computation of the entire single-particle spectrum of correlated molecules is an outstanding challenge in the field of quantum chemistry, with standard density functional theory approaches often giving an inadequate description of excitation energies and gaps. In this work, we expand upon a recently-introduced approach which relies on a fully self-consistent many-body perturbation theory, coupled to a non-perturbative truncation of the effective dynamics at each step. We show that this yields a low-scaling and accurate method across a diverse benchmark test set, capable of treating moderate levels of strong correlation effects, and detail an efficient implementation for applications up to $\sim1000$ orbitals on parallel resources. We then use this method to characterise the spectral properties of the artemisinin anti-malarial drug molecule, resolving discrepancies in previous works concerning the active sites of the lowest energy fundamental excitations of the system.