  Accurate virtual high-throughput screening (VHTS) of transition metal
complexes (TMCs) remains challenging due to the possibility of high
multi-reference (MR) character that complicates property evaluation. We compute
MR diagnostics for over 5,000 ligands present in previously synthesized
transition metal complexes in the Cambridge Structural Database (CSD). To
accomplish this task, we introduce an iterative approach for consistent ligand
charge assignment for ligands in the CSD. Across this set, we observe that MR
character correlates linearly with the inverse value of the averaged bond order
over all bonds in the molecule. We then demonstrate that ligand additivity of
MR character holds in TMCs, which suggests that the TMC MR character can be
inferred from the sum of the MR character of the ligands. Encouraged by this
observation, we leverage ligand additivity and develop a ligand-derived machine
learning representation to train neural networks to predict the MR character of
TMCs from properties of the constituent ligands. This approach yields models
with excellent performance and superior transferability to unseen ligand
chemistry and compositions.
