A major step forward in machine learning of accurate DFAs has been achieved by the development of the Deep Mind 21 (DM21) functional . From the point of view of DFA's classification, DM21 is a machine-learned local hybrid (see Ref. for a very recent comparison between human-designed local hybrids and DM21). With the inclusion of fractional charges (FC) and fractional spin (FS) data in the training, DM21 has addressed some of the long-standing deficiencies of standard DFAs linked to their improper behavior for systems with FC and FS . However, the training of DM21 excludes elements heavier than Krypton, posing questions about its performance in transition metal chemistry (TMC), a realm generally challenging for quantum chemistry due to strong correlation effects and a large number of multireference cases .