Major Contributions
 

1. Combining DFT and machine-learning interatomic potentials, a comprehensive bimetallic catalyst library was established, predicting activity rankings for alkaline HOR; RuIr was identified as the most active and durable catalyst, consistent with experimental results.

2. The theoretical activity correlates with electron-accepting tendencies and H₂/OH* adsorption strengths, providing a predictive design framework validated across ten bimetallic combinations (RuIr > PtRu > AuIr > PtRh > PtIr…).

3. Operando XAS reveals synergistic RuIr chemistry: Ir provides superior electron-accepting ability and H₂ adsorption while Ru facilitates OH* adsorption, collectively accelerating the alkaline HOR mechanism in anion exchange membrane fuel cells.

主要貢獻
 

1. 結合 DFT 與機器學習原子間勢，建立雙金屬催化劑完整資料庫，預測鹼性 HOR 活性排名；RuIr 被確認為最具活性與耐久性的催化劑，與實驗結果一致。

2. 理論活性與電子接受趨勢及 H₂/OH* 吸附強度相關，提供在十種雙金屬組合中驗證的預測設計框架（RuIr > PtRu > AuIr > PtRh > PtIr…）。

3. 原位 XAS 揭示 RuIr 協同機制：Ir 提供強電子接受能力與 H₂ 吸附，Ru 促進 OH* 吸附，共同加速陰離子交換膜燃料電池中的鹼性 HOR 機制。