A number of strategies are being explored to address the increasing global energy demand and environmental challenges, including fuel cells and water electrolyzers. However, the efficiency of such devices is limited by the sluggish oxygen electrochemistry at the catalytic surface. As a prototypical materials discovery challenge in electrochemistry, significant advances have been made in developing Pt-based alloys. However, their performance is limited by the linear scaling relationships between the binding energies of *OOH and *OH surface species. One strategy for overcoming these linear scaling relations is to develop catalysts that allow preferential stabilization of certain reaction intermediates. Metal-organic frameworks (MOFs), which are a class of nanoporous materials consisting of metal nodes inter-connected via organic linkers, are particularly attractive for circumventing the ‘standard’ scaling relationships. Using highly accurate Density Functional Theory (DFT) calculations, we show that porphyrin-based MOFs are promising electrocatalysts for ORR that allow preferential stabilization of *OOH.[1] These materials consist of single transition metal cations (e.g., Co+2, Fe+3 etc.) as the binding site in a 3-D active site environment, which necessitates the development of novel computational methodologies to describe the electronic structure and solvation effects. We will present a novel approach that combines multiscale modeling (i.e., DFT + classical force fields) with machine learning to describe the solvation on the oxygen-based adsorbates in the porous MOF environment. This talk will present an overview of the field and will lay a foundation describing for electrochemical reactions for a number of emerging class of materials.

Circumventing scaling relations in oxygen electrochemistry using metal-organic frameworks

ACS Fall 2021

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