Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining sub eV Error from a Restricted Open-Shell Kohn-Sham Approach

TL;DR

This paper introduces a robust and accurate method using the Square Gradient Minimization algorithm with ROKS to predict core spectra of molecules at DFT cost, achieving sub-eV errors and surpassing traditional TDDFT accuracy.

Contribution

The paper presents a new application of SGM for ROKS excited state optimization, enabling highly accurate core spectra predictions at affordable computational cost.

Findings

Predicts K edge spectra with ~0.3 eV RMS error.

Effective for L edge spectra with spin-orbit correction.

Outperforms traditional TDDFT in accuracy.

Abstract

We present the use of the recently developed Square Gradient Minimization (SGM) algorithm for excited state orbital optimization, to obtain spin-pure Restricted Open-Shell Kohn-Sham (ROKS) energies for core excited states of molecules. The SGM algorithm is robust against variational collapse, and offers a reliable route to converging orbitals for target excited states at only 2-3 times the cost of ground state orbital optimization (per iteration). ROKS/SGM with the modern SCAN/$\omega$B97X-V functionals is found to predict the K edge of C,N,O and F to a root mean squared error of $\sim$0.3 eV. ROKS/SGM is equally effective at predicting L edge spectra of third period elements, provided a perturbative spin-orbit correction is employed. This high accuracy can be contrasted with traditional TDDFT, which typically has greater than 10 eV error and requires translation of computed spectra to align with experiment. ROKS is computationally affordable (having the same scaling as ground state DFT, and a slightly larger prefactor) and can be applied to geometry optimizations/ab-initio molecular dynamics of core excited states, as well as condensed phase simulations. ROKS can also model doubly excited/ionized states with one broken electron pair, which are beyond the ability of linear response based methods.