Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers
This work addresses the costly development of catalysts for CO2 conversion, offering a computational method to accelerate discovery, though it is incremental as it builds on existing machine learning and catalysis techniques.
The researchers tackled the challenge of efficiently exploring catalysts for CO2 hydrogenation by developing a machine learning workflow to analyze transition states at inverse catalysts, revealing structure-activity trends and breaking linear scaling relations that explain experimental performance.
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.