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Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning

The rational tailoring of transition metal complexes is necessary to address outstanding challenges in energy utilization and storage. Heterobimetallic transition metal complexes that exhibit metal-metal bonding in stacked "double decker" ligand structures are an emerging, attractive platform for catalysis, but their properties are challenging to predict prior to laborious synthetic efforts. We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for rational bimetallic complex design. We tailor graph-based representations of the metal-local environment for these heterobimetallic complexes for use in training of multiple linear regression and kernel ridge regression (KRR) models. Focusing on oxidation potentials, we obtain a set of 28 experimentally characterized complexes to develop a multiple linear regression model. On this training set, we achieve good accuracy (mean absolute error, MAE, of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We trained a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of complexes. Our work provides guidance for rational bimetallic design, suggesting that properties including the formal shortness ratio should be transferable from one period to another.

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