An AI-Powered Methodology for Atomic-Scale Analysis of Heterogenized Correlated Single-Atom Catalysts.

Correlated single-atom catalysts offer transformative potential in catalysis, particularly in the field of electrocatalysis, with a focus on oxygen evolution reactions. Advanced characterization is critical to understanding their atomic-scale properties when techniques usually used in molecular science (Nuclear Magnetic Resonance (NMR), X-ray Diffraction (XRD), Infrared spectroscopy (IR), or Mass Spectrometry (MS)) cannot be applied after dispersing them on a carrier material. Here, a methodology that combines machine learning and mathematical optimization techniques to detect and quantify metal-metal interactions within heterobinuclear Au(III)-Pd(II) macrocyclic complexes on atomically resolved high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) images is introduced. Both supervised and unsupervised machine learning methods are evaluated, with the U-net architecture demonstrating superior performance in distinguishing the two involved chemical species. Mathematical optimization models further enhance the reliability of metal pair identification by providing precise distance metrics for the pairs. This methodology allows for the study of both the dynamics and bond interaction of heterobinuclear Au(III)-Pd(II) complexes. Notably, the analysis of time series of images reveals that most metal pairs remained stable under the high-energy electron beam irradiation conditions. Likewise, the Au-Pd distance within the pairs remains unchanged, indicating a robust interaction of the two metals with the ligand even after being deposited on the amorphous carbon substrate.