Universal Catalyst Design Framework for Electrochemical Hydrogen Peroxide Synthesis Facilitated by Local Atomic Environment Descriptors

TL;DR

This paper introduces a universal framework combining local atomic environment descriptors, machine learning, and microkinetic modeling to efficiently design high-performance catalysts for electrochemical hydrogen peroxide synthesis across diverse material categories.

Contribution

The study develops a novel framework integrating weighted Atomic Center Symmetry Function descriptors with machine learning and high-throughput screening, enabling universal catalyst design across multiple material types.

Findings

ML models predict adsorption energies with high accuracy (R2 > 0.84).

The framework accelerates catalyst screening and design.

A universal microkinetic volcano model aligns with experimental data.

Abstract

Developing a universal and precise design framework is crucial to search high-performance catalysts, but it remains a giant challenge due to the diverse structures and sites across various types of catalysts. To address this challenge, herein, we developed a novel framework by the refined local atomic environment descriptors (i.e., weighted Atomic Center Symmetry Function, wACSF) combined with machine learning (ML), microkinetic modeling, and computational high-throughput screening. This framework is successfully integrated into the Digital Catalysis Database (DigCat), enabling efficient screening for 2e- water oxidation reaction (2e- WOR) catalysts across four material categories (i.e., metal alloys, metal oxides and perovskites, and single-atom catalysts) within a ML model. The proposed wACSF descriptors integrating both geometric and chemical features are proven effective in predicting the adsorption free energies with ML. Excitingly, based on the wACSF descriptors, the ML models accurately predict the adsorption free energies of hydroxyl ({\Delta}GOH*) and oxygen ({\Delta}GO*) for such a wide range of catalysts, achieving R2 values of 0.84 and 0.91, respectively. Through density functional theory calculations and microkinetic modeling, a universal 2e- WOR microkinetic volcano model was derived with excellent agreement with experimental observations reported to date, which was further used to rapidly screen high-performance catalysts with the input of ML-predicted {\Delta}GOH*. Most importantly, this universal framework can significantly improve the efficiency of catalyst design by considering multiple types of materials at the same time, which can dramatically accelerate the screening of high-performance catalysts.