Advances in the theoretical understanding of electrochemical systems have, over the past decade, led to growing use of periodic Density Functional Theory studies to treat a surprisingly large ensemble of electrocatalytic reactions, ranging from carbon dioxide electroreduction to oxygen evolution.  Many such studies have employed simplified models of the electrochemical environment to determine reactivity trends across a broad space of catalytic materials, while other efforts have focused on incorporating more detailed kinetic effects, such as coverage effects, structure sensitivity, and non-mean field effects, into the theoretical predictions.  An emerging challenge is to combine these approaches to ultimately enable theoretical design of electrocatalysts for reactions of significantly expanded chemical and materials complexity.<br/>In this talk, I will begin by discussing how we have applied strategies from computational heterogeneous catalysis to design enhanced electrocatalysts for the classic oxygen reduction reaction.  I will then explore challenges in obtaining more detailed descriptions of the electrocatalytic reaction environment, focusing in particular on how high coverages of adsorbates may be efficiently described on low symmetry alloy and defect surfaces.  I will conclude with some perspectives on how these complexities may be incorporated into traditional computational catalyst screening approaches to identify improved materials for more complex electrocatalytic systems.

Reactivity trends in heterogeneous electrocatalysis – combined first principles and machine learning approach to probe effects of coverage and structure sensitivity

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.