Capturing the Complexities of Catalyst–Support Interactions with the Help of Machine Learning

Abstract The structure of metal nanoparticles is central to their catalytic activity, but metal–support interactions are difficult to model via quantum‐mechanical calculations. Using a machine‐learned potential to model supported silver nanoparticles, it has been shown that the idealized nanoparticle shapes commonly invoked in the literature do not reflect experiments for diameters below 8 nm, as reported by Maxson and Szilvási.