Active Learning Accelerating to Screen Dual-Metal-Site Catalysts for Electrochemical Carbon Dioxide Reduction Reaction,ACS Applied Materials & Interfaces

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Active Learning Accelerating to Screen Dual-Metal-Site Catalysts for Electrochemical Carbon Dioxide Reduction Reaction
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2023-02-28 , DOI: 10.1021/acsami.2c21332
Hu Ding ₁ , Yawen Shi ₁ , Zeyang Li ₁ , Si Wang ₁ , Yujie Liang ₁ , Haisong Feng ₁ , Yuan Deng ₁ , Xin Song ₁ , Pengxin Pu ₁ , Xin Zhang ₁

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Dual-metal-site catalysts (DMSCs) are increasingly important catalysts in the field of electrochemical carbon dioxide reduction reaction (CO₂RR) in recent years. However, rapid screening of suitable metal combinations of DMSCs remains a huge challenge. Herein, we constructed an active learning (AL) framework to study CO₂RR to HCOOH. This AL framework turned out a success in the accurate prediction of 282 DMSCs for CO₂RR through interactive learning between users and machine learning (ML) models. Among the 42 DMSCs calculated in three iteration loops of AL, 29 DMSCs were obtained, where the screening success rate was as high as 70%. Furthermore, we found five experimentally unexplored DMSCs that exhibited better CO₂RR activity and selectivity than pure Bi. Low prediction errors on other DMSCs show that the AL model possessed outstanding universality. The results prove the excellent potential of the AL method and provide guidance on the design of high-performance electrocatalysts for CO₂RR.

中文翻译：

主动学习加速筛选用于电化学二氧化碳还原反应的双金属位点催化剂

双金属位点催化剂（DMSCs）是近年来电化学二氧化碳还原反应（CO ₂ RR）领域中越来越重要的催化剂。然而，快速筛选合适的DMSC金属组合仍然是一个巨大的挑战。在此，我们构建了一个主动学习 (AL) 框架来研究 CO ₂ RR 到 HCOOH。该 AL 框架通过用户与机器学习 (ML) 模型之间的交互学习，成功准确预测了 282 个 DMSC 的 CO _{2 RR。}在AL的3次迭代循环中计算得到的42个DMSCs中，得到了29个DMSCs，筛选成功率高达70%。此外，我们还发现了五个未经实验探索的 DMSC，它们表现出更好的 CO ₂RR活性和选择性高于纯Bi。其他 DMSC 的低预测误差表明 AL 模型具有突出的普适性。_{结果证明了 AL 方法的卓越潜力，并为 CO 2} RR的高性能电催化剂的设计提供了指导。

更新日期：2023-02-28

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