Performance comparison of r2SCAN and SCAN metaGGA density functionals for solid materials via an automated, high-throughput computational workflow

Computational materials discovery efforts utilize hundreds or thousands of density functional theory calculations to predict material properties. Historically, such efforts have performed calculations at the generalized gradient approximation (GGA) level of theory due to its efficient compromise between accuracy and computational reliability. However, high-throughput calculations at the higher metaGGA level of theory are becoming feasible. The strongly constrained and appropriately normed (SCAN) metaGGA functional offers superior accuracy to GGA across much of chemical space, making it appealing as a general-purpose metaGGA functional, but it suffers from numerical instabilities that impede its use in high-throughput workflows. The recently developed ${\mathrm{r}}^{2}\mathrm{SCAN}$ metaGGA functional promises accuracy similar to SCAN in addition to more robust numerical performance. However, its performance compared to SCAN has yet to be evaluated over a large group of solid materials. In this paper, we compared ${\mathrm{r}}^{2}\mathrm{SCAN}$ and SCAN predictions for key properties of approximately 6000 solid materials using a newly developed high-throughput computational workflow. We find that ${\mathrm{r}}^{2}\mathrm{SCAN}$ predicts formation energies more accurately than SCAN and PBEsol for both strongly and weakly bound materials and that ${\mathrm{r}}^{2}\mathrm{SCAN}$ predicts systematically larger lattice constants than SCAN. We also find that ${\mathrm{r}}^{2}\mathrm{SCAN}$ requires modestly fewer computational resources than SCAN and offers significantly more reliable convergence. Thus, our large-scale benchmark confirms that ${\mathrm{r}}^{2}\mathrm{SCAN}$ has delivered on its promises of numerical efficiency and accuracy, making it a preferred choice for high-throughput metaGGA calculations.