r2SCAN-D4: Dispersion corrected meta-generalized gradient approximation for general chemical applications

Abstract

We combine a regularized variant of the strongly constrained and appropriately normed semilocal density functional [J. Sun, A. Ruzsinszky, and J. P. Perdew, Phys. Rev. Lett. 115, 036402 (2015)] with the latest generation semi-classical London dispersion correction. The resulting density functional approximation r2SCAN-D4 has the speed of generalized gradient approximations while approaching the accuracy of hybrid functionals for general chemical applications. We demonstrate its numerical robustness in real-life settings and benchmark molecular geometries, general main group and organo-metallic thermochemistry, and non-covalent interactions in supramolecular complexes and molecular crystals. Main group and transition metal bond lengths have errors of just 0.8%, which is competitive with hybrid functionals for main group molecules and outperforms them for transition metal complexes. The weighted mean absolute deviation (WTMAD2) on the large GMTKN55 database of chemical properties is exceptionally small at 7.5 kcal/mol. This also holds for metal organic reactions with an MAD of 3.3 kcal/mol. The versatile applicability to organic and metal–organic systems transfers to condensed systems, where lattice energies of molecular crystals are within the chemical accuracy (errors <1 kcal/mol).

Pure and Hybrid SCAN, rSCAN, and r2SCAN: Which One Is Preferred in KS- and HF-DFT Calculations, and How Does D4 Dispersion Correction Affect This Ranking?

“…All the original and the refitted D4 parameters are listed in Table S1 in Supplementary Materials . We note in passing that in a recent study, Ehlert et al [ 39 ] have reported a set of D4 parameters only for the self-consistent rSCAN and r 2 SCAN pure mGGA functionals. However, the set used by Ehlert et al for optimizing the D4 parameters is different from what we have used in this study.…”

Dispersion Corrected r2SCAN Based Global Hybrid Functionals: r2SCANh, r2SCAN0, and r2SCAN50

“…While Santra and Martin reported values around three for the s 8 , we found smaller and more physical values < 1, which is in line with previous parameterizations of the rational damping function for SCAN 10 and r 2 SCAN. 14 To investigate the effect of this discrepancy we evaluated all benchmark sets tested for this work with the parameters proposed by Santra and Martin as well.…”

“…Mostly, the r 2 SCANx-D4 hybrids yield slightly worse results compared to the already very well performing r 2 SCAN-D4. 14 In the context of the higher computational demand of the hybrid functionals, geometry optimizations using such may not be recommended if no strong SIE effects are expected. Alternatively, the original r 2 SCAN-D4 or its even more efficient composite variant r 2 SCAN-3c 15 may be applied instead.…”

“…This issue is resolved with the regularized SCAN (rSCAN) 11 and the subsequent r 2 SCAN functional. 12,13 Inspired by the excellent performance of r 2 SCAN, its London dispersion corrected variants, 14 and the composite DFT method r 2 SCAN-3c, 15 we present three global hybrid functional variants of r 2 SCAN termed r 2 SCANh, r 2 SCAN0, and r 2 SCAN50 with 10%, 25%, and 50% of HFX admixture, respectively. Matching parameters for the D4, 9,16 the D3(BJ), 17,18 and the non-self consistent VV10 19 London dispersion correction are provided.…”

Free and Open Source Software for Computational Chemistry Education

“…Although hybrid functionals are computationally more demanding than nonhybrid functionals, it is notable that the dispersioncorrected hybrid PBE0-D4 generalized gradient approximation (GGA) functional was recently shown to outperform the dispersion-corrected, meta-GGA-type nonhybrid r 2 SCAN-D4 functional in accuracy even for reaction energies of metal-organic reactions. 232 Having completed our introduction to DFT calculations, basis sets, and NWChem, similarly to the workflow in the case of xtb, the first task is to bring both molecules into a (local) minimum of the potential energy surface (PES) by means of geometry optimization. The geometry optimization is started from the same hand-built initial geometries presented in Subsection IV A.…”