催化作用
质子化
反应机理
化学
离解(化学)
工作(物理)
机制(生物学)
氧还原反应
氧气
过渡金属
透视图(图形)
过渡状态
无机化学
氧化还原
反应中间体
化学动力学
材料科学
动力学
计算化学
纳米技术
化学反应
动能
还原(数学)
反应速率
火山
反应动力学
化学工程
多相催化
氧还原
作者
Jin Liu,Hao Li,Haoxiang Xu,Daojian Cheng
摘要
ABSTRACT Dual‐atom catalysts (DACs) have demonstrated superior potential in the oxygen reduction reaction (ORR). However, the single‐peak activity volcano derived from classical associative mechanism is contrast to the large‐scale experimental data from the Digital Catalysis Platform ( DigCat ). Herein, we studied ORR over 200 DACs from thermodynamic and kinetic perspectives, and found that the dissociative mechanism is generally dominant for DACs. By integrating potential‐related microkinetic modeling and machine learning (ML)‐derived interpretable structural descriptors, we discovered a dual‐ Sabatier optima volcano map against Δ G (OH*) (or structural descriptors), which was rigorously validated against available experimental data. Dual‐ Sabatier optima stem from the rate‐determining step of dissociative mechanism switching among three elementary reactions (O 2 dissociation → 2OH protonation → OH protonation), which can be extended across DACs containing transition metal, metal‐like, and non‐metal elements as center atoms. It opens a brand‐new perspective for rational design of DACs and atomically dispersed catalysts for other reactions beyond ORR, of which the dominant reaction mechanism may be different from single‐atom catalysts (SACs) and lead to diverse activity volcano maps. Most importantly, this work illustrates that new phenomenon can be identified from “old experimental data” under a large data scale, with the help of theoretical simulations integrated with interpretable ML .
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