Adsorption energies are necessary but not sufficient to identify good catalysts

TL;DR

This paper evaluates the effectiveness of adsorption energies and overpotential estimates in identifying good catalysts, revealing that high uncertainty limits their reliability and suggesting alternative metrics for catalyst screening.

Contribution

It systematically quantifies the uncertainty in predicting adsorption energies and demonstrates the limitations of overpotential as a sole screening metric for catalysts.

Findings

Overpotential estimates can misclassify catalysts due to uncertainty.

Adsorption energy predictions have an uncertainty of about 0.3-0.5 eV.

Reliance solely on overpotential is insufficient for catalyst discovery.

Abstract

As a core technology for green chemical synthesis and electrochemical energy storage, electrocatalysis is central to decarbonization strategies aimed at combating climate change. In this context, computational and machine learning driven catalyst discovery has emerged as a major research focus. These approaches frequently use the thermodynamic overpotential, calculated from adsorption free energies of reaction intermediates, as a key parameter in their analysis. In this paper, we explore the large-scale applicability of such overpotential estimates for identifying good catalyst candidates by using datasets from the Open Catalyst Project (OC20 and OC22). We start by quantifying the uncertainty in predicting adsorption energies using \textit{ab initio} methods and find that $\sim$0.3-0.5 eV is a conservative estimate for a single adsorption energy prediction. We then compute the overpotential of all materials in the OC20 and OC22 datasets for the hydrogen and oxygen evolution reactions. We find that while the overpotential allows the identification of known good catalysts such as platinum and iridium oxides, the uncertainty is large enough to misclassify a broad fraction of the datasets as ``good'', which limits its value as a screening criterion. These results question the reliance on overpotential estimation as a primary evaluation metric to sort through catalyst candidates and calls for a shift in focus in the computational catalysis and machine learning communities towards other metrics such as synthesizability, stability, lifetime or affordability.