Assessing the Performance of Recent Density Functionals for Bulk Solids
Gabor I. Csonka, John P. Perdew, Adrienn Ruzsinszky, Pier H. T., Philipsen, Sebastien Lebegue, Joachim Paier, Oleg A. Vydrov, and Janos G., Angyan

TL;DR
This study evaluates recent density functionals' accuracy in predicting bulk properties of solids, highlighting their strengths and limitations compared to traditional functionals, with implications for computational materials science.
Contribution
It provides a comprehensive comparison of recent and standard density functionals for solids, including analysis of their accuracy and the factors influencing their performance.
Findings
PBEsol and AM05 perform well for bulk properties.
PBE outperforms PBEsol in cohesive energy overall.
Gaussian basis sets and experimental data quality affect functional assessment.
Abstract
We assess the performance of recent density functionals for the exchange-correlation energy of a nonmolecular solid, by applying accurate calculations with the GAUSSIAN, BAND, and VASP codes to a test set of 24 solid metals and non-metals. The functionals tested are the modified Perdew-Burke-Ernzerhof generalized gradient approximation (PBEsol GGA), the second-order GGA (SOGGA), and the Armiento-Mattsson 2005 (AM05) GGA. For completeness, we also test more-standard functionals: the local density approximation, the original PBE GGA, and the Tao-Perdew-Staroverov-Scuseria (TPSS) meta-GGA. We find that the recent density functionals for solids reach a high accuracy for bulk properties (lattice constant and bulk modulus). For the cohesive energy, PBE is better than PBEsol overall, as expected, but PBEsol is actually better for the alkali metals and alkali halides. For fair comparison of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · Surface and Thin Film Phenomena · Superconductivity in MgB2 and Alloys
