Moving DFAs beyond the "no man's land" by creating machine-learned functionals with a broad applicability to both main-group and transition metal chemistry remains an open challenge. On the one hand, incorporating features designed to capture strong correlation effects into machine-learning DFAs may improve the transferability to transition metal chemistry . On the other hand, addressing this by incorporating transition metal reactions into machine-learning density functionals comes with its own obstacles: