Published in

American Institute of Physics, The Journal of Chemical Physics, 14(140), p. 144107

DOI: 10.1063/1.4870397

Links

Tools

Export citation

Search in Google Scholar

MBEEF: An accurate semi-local Bayesian error estimation density functional

Journal article published in 2014 by Jess Wellendorff, Keld T. Lundgaard, Karsten W. Jacobsen, Thomas Bligaard
This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Orange circle
Published version: archiving restricted
Data provided by SHERPA/RoMEO

Abstract

We present a general-purpose meta-generalized gradient approximation (MGGA) exchange-correlation functional generated within the Bayesian error estimation functional framework [J. Wellendorff, K. T. Lundgaard, A. Møgelhøj, V. Petzold, D. D. Landis, J. K. Nørskov, T. Bligaard, and K. W. Jacobsen, Phys. Rev. B 85, 235149 (2012)]. The functional is designed to give reasonably accurate density functional theory (DFT) predictions of a broad range of properties in materials physics and chemistry, while exhibiting a high degree of transferability. Particularly, it improves upon solid cohesive energies and lattice constants over the BEEF-vdW functional without compromising high performance on adsorption and reaction energies. We thus expect it to be particularly well-suited for studies in surface science and catalysis. An ensemble of functionals for error estimation in DFT is an intrinsic feature of exchange-correlation models designed this way, and we show how the Bayesian ensemble may provide a systematic analysis of the reliability of DFT based simulations.