Computational protocol for modeling supported nanoparticle catalysts with strong metal-support interactions

Modeling supported nanoparticle catalysts with strong metal-support interactions (SMSI) is challenging due to their high structural complexity. Here, we present a computational protocol that uses a modular and adaptive Python-based framework to achieve this goal. We describe steps for constructing supported nanoparticle models and exploring large configurational spaces of adsorption states. We then detail procedures for guiding the implementation of analysis workflows to identify catalytically relevant configurations by matching simulated and experimental infrared (IR) spectra. For complete details on the use and execution of this protocol, please refer to G. Valadez Huerta et al. 1