Assessment of density functional methods with correct asymptotic behavior

TL;DR

This paper compares long-range corrected hybrid functionals and asymptotically corrected model potentials, showing LC hybrids generally outperform AC models across various applications, and introduces new benchmark databases for assessing density functional methods.

Contribution

It provides a comprehensive performance comparison of LC hybrid and AC model potential schemes and introduces new benchmark databases for evaluating density functional methods.

Findings

LC hybrid scheme outperforms AC model potential scheme

Expanded databases for electron affinities and atomization energies

New benchmark database AE113 for atomization energies

Abstract

Long-range corrected (LC) hybrid functionals and asymptotically corrected (AC) model potentials are two distinct density functional methods with correct asymptotic behavior. They are known to be accurate for properties that are sensitive to the asymptote of the exchange-correlation potential, such as the highest occupied molecular orbital energies and Rydberg excitation energies of molecules. To provide a comprehensive comparison, we investigate the performance of the two schemes and others on a very wide range of applications, including the asymptote problems, self-interaction-error problems, energy-gap problems, charge-transfer problems, and many others. The LC hybrid scheme is shown to consistently outperform the AC model potential scheme. In addition, to be consistent with the molecules collected in the IP131 database [Y.-S. Lin, C.-W. Tsai, G.-D. Li, and J.-D. Chai, J. Chem. Phys., 2012, 136, 154109], we expand the EA115 and FG115 databases to include, respectively, the vertical electron affinities and fundamental gaps of the additional 16 molecules, and develop a new database AE113 (113 atomization energies), consisting of accurate reference values for the atomization energies of the 113 molecules in IP131. These databases will be useful for assessing the accuracy of density functional methods.