Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H2O2 Synthesis through Active Motif Screening

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.

相较于当前高能耗的蒽醌法生产工艺，氧气（O₂）的电化学还原是一条清洁且可实现分布式制备过氧化氢（H₂O₂）的技术路径。氧气电化学还原可通过两电子或四电子路径进行，因此调控反应选择性并最大化催化活性至关重要。Siahrostami等人[《自然·材料》(Nat. Mater.) 2013, 12, 1137]提出了一种调控反应路径的全新策略：通过优化吸附基元组合以调节反应中间体的吸附位点，基于密度泛函理论（DFT）计算筛选出Pt-Hg催化剂，并通过实验验证了该催化剂的性能。受此研究思路启发，本研究采用当前前沿的高通量筛选技术，开发用于高选择性制备H₂O₂的氧气还原催化剂。本研究从材料项目（Materials Project）数据库出发，对候选催化剂的活性、选择性及电化学稳定性进行系统评估。为高效推进筛选流程，我们提出了一种基于活性基元的筛选方法：预先剔除无应用潜力的材料，仅对具备研发价值的候选体开展DFT计算，大幅降低了所需的计算工作量。最后，我们探讨了一种可用于未来高效高通量筛选的机器学习流程策略，该流程由非线性降维和基于密度的聚类两个模块构成。