The success of computational material design for sustainable energy catalysis

Abstract

Computational material design (CMD) employs quantum mechanical simulations, density functional theory, and machine learning techniques to correlate electronic structural attributes with physical and chemical properties of materials. Over the last decade, CMD has proven to be critical to the advancement of materials science and a variety of engineering fields. This contribution provides an overview of CMD’s success in driving materials discovery for catalysis in the context of sustainable energy applications. Specifically, we discuss how CMD has enabled the development of catalysts for three electrochemical processes that are critical to sustainable energy applications, oxygen reduction reactions, water oxidation reactions, and CO2 reduction reactions. We illustrate how CMD provides a powerful and efficient method for understanding underlying reaction mechanisms as well as predicting and optimizing catalyst properties. Furthermore, we demonstrate how this strategic approach has enabled researchers to effectively navigate the vast chemical space of potential catalysts and rapidly identify novel materials possessing desirable electronic structures and catalytic activity.