Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands

Accurate virtual high-throughput screening (VHTS) of
transition
metal complexes (TMCs) remains challenging due to the possibility
of high multireference (MR) character that complicates property evaluation.
We compute MR diagnostics for over 5,000 ligands present in previously
synthesized octahedral mononuclear 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 the 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 the 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.