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Na Group’s ML-based catalyst design research is discussed in an issue of ACS Catalysis (IF: 12.350, JCR: 7.233%). This work is in collaboration with Prof. Back at Sogang University.

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Abstract

Electrochemical reduction of O2 provides a clean and decentralized pathway to produce H2O2 compared to the current energy-intensive anthraquinone process. As the electrochemical reduction of O2 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 O2 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.

All surfaces considered in this work of which H2O2 were evaluated. (A) Two-dimensional latent space visualized by UMAP. Location in the latent space represents similarities in active motifs. Several representative cluster indices categorized by DBSCAN are highlighted. (B and C) Example atomic structures of catalytically (B) active and (C) inactive clusters in terms of H2O2 . (D to F) Cluster characterization with (D) box plot of H2O2 distribution and occurrence heat maps of (E) inactive and (F) active elements in each cluster.

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