초록/요약

Electrochemical reduction of O-2 provides a clean and decentralized pathway to produce H2O2 compared to the current energy-intensive anthraquinone process. As the electrochemical reduction of O-2 proceeds via either a two-electron or a four-electron pathway, it is thus essential to control the selectivity as well as to maximize the catalytic activity. Siahrostami et al. [Nat. Mater. 2013, 12, 1137] demonstrated a novel approach to control the reaction pathway by optimizing an adsorption ensemble to tune adsorption sites of reaction intermediates, identified Pt-Hg catalysts from density functional theory (DFT) calculations, and experimentally validated this catalyst. Inspired by this concept, in this work, we apply a state-of-the-art high-throughput screening to develop an O-2 reduction catalyst for selective H2O2 production. Starting from the Materials Project database, we evaluate activity, selectivity, and electrochemical stability. To efficiently perform the screening, we introduce an active-motif-based approach, which pre-screens unpromising materials and performs DFT calculations only for promising materials, which significantly reduces the number of the required calculations. Finally, we discuss a strategy for efficient future high-throughput screening using a machine learning pipeline consisting of a nonlinear dimension reduction and a density-based clustering.