The conversion of $\mathrm{CO_2}$ into useful products such as methanol is a
key strategy for abating climate change and our dependence on fossil fuels.
Developing new catalysts for this process is costly and time-consuming and can
thus benefit from computational exploration of possible active sites. However,
this is complicated by the complexity of the materials and reaction networks.
Here, we present a workflow for exploring transition states of elementary
reaction steps at inverse catalysts, which is based on the training of a neural
network-based machine learning interatomic potential. We focus on the crucial
formate intermediate and its formation over nanoclusters of indium oxide
supported on Cu(111). The speedup compared to an approach purely based on
density functional theory allows us to probe a wide variety of active sites
found at nanoclusters of different sizes and stoichiometries. Analysis of the
obtained set of transition state geometries reveals different
structure–activity trends at the edge or interior of the nanoclusters.
Furthermore, the identified geometries allow for the breaking of linear scaling
relations, which could be a key underlying reason for the excellent catalytic
performance of inverse catalysts observed in experiments.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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