Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States

Quantum–mechanical transition states can aid in the identification of promising catalysts for methane C–H activation and functionalization. However, only a limited amount of the vast metal–ligand chemical space has been computationally evaluated. To begin to solve this problem, we showcase a workflow that combines automated construction of Pt(II)-ligand combinations and automated transition-state searching with machine learning to maximize the generation of fully optimized transition states.