Inverse Design of Ligands Using A Deep Generative Model Semi-supervised by A Data-driven Ligand Field Strength Metric

Transition metal (TM) complexes exhibit diverse structural and electronic properties. The properties of a TM complex can be tuned through modulating the ligand field strength (LFS) inflicted by its ligands. Current quantification of the LFS of a ligand is mainly derived from experimental measurements on a subset of highly symmetrical TM complexes and is limited in ligand scope. Herein, we report a data-driven method to quantify the LFS of ligands assigned from experimental crystal structures of TM complexes. We first show that the experimental metal-ligand bond lengths of over 4000 mononuclear Fe, Co, and Mn complexes form bimodal distributions. Using gaussian fits on the bimodal distributions, each TM complex is assigned with a spin state label. These spin state labels can then be used to calculate the LFS of the ligands of the complexes. Using the obtained data-driven LFS metric, we establish that a semi-supervised deep generative model, junction tree variational autoencoder (JTVAE), can be employed to predict LFS values. Our model exhibits a mean absolute error (MAE) of 0.047 and root mean squared error of 0.072 on the training set. The model also allows the generation of novel ligands with desirable LFS values.