r²SCAN-D4

r²SCAN-D4 is an ab initio density-functional theory (DFT) method from Sebastian Ehlert at the University of Bonn and co-workers at Tulane University, Temple University, Biovia, and Merck KGaA in 2021.

Read the r²SCAN-D4 preprint. Read the r²SCAN-D4 paper.

r²SCAN-D4's TorsionNet206 Results

TorsionNet206 is a high-accuracy molecular benchmark that evaluates the accuracy of predicting and ranking of dihedral energy profiles. The benchmark contains 206 subsets of 24 conformers each computed at the CCSD(T)/def2-TZVP level of theory. Mean absolute error (MAE) and root mean square error (RMSE) are shown in kcal/mol. See all TorsionNet206 results.