Maximal predictability approach for identifying the right descriptors for electrocatalytic reactions

TL;DR

This paper introduces a rigorous method to incorporate uncertainty in DFT-based activity predictions for electrocatalytic reactions, improving the identification of high-activity materials by using expected activity and prediction efficiency.

Contribution

It develops a framework to propagate DFT uncertainty into activity models, enabling more reliable material screening for electrocatalysis.

Findings

Uncertainty propagation improves material distinguishability.

Prediction efficiency quantifies activity discrimination capability.

Framework applied to four key electrochemical reactions.

Abstract

Density Functional Theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations have finite uncertainty and this raises an issue related to the ability to delineate materials that possess high activity. With the development of error estimation capabilities in DFT, there is an urgent need to propagate uncertainty through activity prediction models. In this work, we demonstrate a rigorous approach to propagate uncertainty within thermodynamic activity models. This maps the calculated activity into a probability distribution, and can be used to calculate the expectation value of the distribution, termed as the expected activity. We prove that the ability to distinguish materials increases with reducing uncertainty. We define a quantity, prediction efficiency, which provides a precise measure of the ability to distinguish the activity of materials for a reaction scheme over an activity range. We demonstrate the framework for 4 important electrochemical reactions, hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. We argue that future studies should utilize the expected activity and prediction efficiency to improve the likelihood of identifying material candidates that can possess high activity.