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Toward chemical accuracy at low computational cost: Density-functional theory with <i> <b> <i>σ</i> </b> </i>-functionals for the correlation energy

Egor Trushin, Adrian Thierbach, Andreas Görling

2021The Journal of Chemical Physics38 citationsDOI

Abstract

We introduce new functionals for the Kohn-Sham correlation energy that are based on the adiabatic-connection fluctuation-dissipation (ACFD) theorem and are named σ-functionals. Like in the well-established direct random phase approximation (dRPA), σ-functionals require as input exclusively eigenvalues σ of the frequency-dependent KS response function. In the new functionals, functions of σ replace the σ-dependent dRPA expression in the coupling-constant and frequency integrations contained in the ACFD theorem. We optimize σ-functionals with the help of reference sets for atomization, reaction, transition state, and non-covalent interaction energies. The optimized functionals are to be used in a post-self-consistent way using orbitals and eigenvalues from conventional Kohn-Sham calculations employing the exchange-correlation functional of Perdew, Burke, and Ernzerhof. The accuracy of the presented approach is much higher than that of dRPA methods and is comparable to that of high-level wave function methods. Reaction and transition state energies from σ-functionals exhibit accuracies close to 1 kcal/mol and thus approach chemical accuracy. For the 10 966 reactions of the W4-11RE reference set, the mean absolute deviation is 1.25 kcal/mol compared to 3.21 kcal/mol in the dRPA case. Non-covalent binding energies are accurate to a few tenths of a kcal/mol. The presented approach is highly efficient, and the post-self-consistent calculation of the total energy requires less computational time than a density-functional calculation with a hybrid functional and thus can be easily carried out routinely. σ-Functionals can be implemented in any existing dRPA code with negligible programming effort.

Topics & Concepts

Density functional theoryAdiabatic processAtomic orbitalEigenvalues and eigenvectorsHybrid functionalRandom phase approximationFunction (biology)PhysicsWave functionEnergy (signal processing)Quantum mechanicsComputational chemistryStatistical physicsMathematicsChemistryEvolutionary biologyElectronBiologyAdvanced Chemical Physics StudiesSpectroscopy and Quantum Chemical StudiesMachine Learning in Materials Science