Applications of sDFT for bulk systems critically depend on the accuracy of the employed NAKE approximation. For molecular dynamics simulations of liquid water, there has been some confusion whether conventional GGA functional are applicable. While initially Hutter and coworkers found that the conventional GGA NAKEs (in particular PW91k, LLP, and PBE) result in too weak hydrogen bonds and the absence of a structured second solvation shell , this finding could not be confirmed in later work. Pavanello demonstrated that GGA NAKEs that perform well for the water dimer, such as revAPBEK, are able to reproduce the radial distribution function of liquid water (including a structured second solvation shell) accurately in sDFT molecular dynamics simulations . The authors also claimed that sDFT might be superior to conventional KS-DFT as it avoids unphysical charge delocalization and thus partially cancels the inter-molecular self-interaction error of GGA xc-functionals. This was demonstrated for molecular dynamics simulations of a hydroxyl radical in aqueous solution. Recently, Pauletti et al. combined sDFT with a ∆-machine learning correction based on Behler's neural network potentials , which was fitted to reproduce KS-DFT.