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

TL;DR

This paper introduces a machine learning workflow to predict adsorption energies at metal-oxide interfaces, reducing computational costs and revealing key electronic and structural factors influencing adsorption for catalyst design.

Contribution

It presents an interpretable ML approach combined with DFT data to efficiently predict adsorption energies and understand underlying factors at metal-oxide interfaces.

Findings

ML models accurately predict adsorption energies outside training data

Identified key electronic and structural factors influencing adsorption

Workflow accelerates catalyst screening and design processes

Abstract

The conversion of $\mathrm{CO_2}$ to value-added compounds is an important part of the effort to store and reuse atmospheric $\mathrm{CO_2}$ emissions. Here we focus on $\mathrm{CO_2}$ 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 data set is used to explore interpretable and black-box ML models with the aim to reveal 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 data set. 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.