Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation
Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train the first machine learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of non-local, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing both expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing d-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty aware evolutionary optimization using the ANN to explore a > 37,000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counter-intuitive oxo-formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.