Interpretable machine learned predictions of adsorption energies at the metal–oxide interface

Abstract

The conversion of CO2 to value-added compounds is an important part of the effort to store and reuse atmospheric CO2 emissions. Here, we focus on CO2 hydrogenation over so-called inverse catalysts: transition metal oxide clusters supported on metal surfaces. The conventional approach for computational screening of such candidate catalyst materials involves a reliance on density functional theory (DFT) to obtain accurate adsorption energies at a significant computational cost. Here, we present a machine learning (ML)-accelerated workflow for obtaining adsorption energies at the metal–oxide interface. We enumerate possible binding sites at the clusters and use DFT to sample a subset of these with diverse local adsorbate environments. The dataset is used to explore interpretable and black-box ML models with the aim of revealing the electronic and structural factors controlling adsorption at metal–oxide interfaces. Furthermore, the explored ML models can be used for low-cost prediction of adsorption energies on structures outside of the original training dataset. The workflow presented here, along with the insights into trends in adsorption energies at metal–oxide interfaces, will be useful for identifying active sites, predicting parameters required for microkinetic modeling of reactions on complex catalyst materials, and accelerating data-driven catalyst design.