Citation: Jieyu Liu, Junze Zhang, Haigang Deng, Shuoao Wang, Xingxing Jiang, Li Wang, Changhong Wang. Understanding the activity origin of Pd-anchored single-atom alloy catalysts for NO-to-NH₃ conversion by DFT studies and machine learning[J]. Chinese Chemical Letters, ;2025, 36(12): 110656. doi: 10.1016/j.cclet.2024.110656 shu

Understanding the activity origin of Pd-anchored single-atom alloy catalysts for NO-to-NH₃ conversion by DFT studies and machine learning

a.
Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control, College of Engineering, Hebei Normal University, Shijiazhuang 050024, China
b.
Functional Crystals Lab, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
c.
Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou 350207, China

chwang@hebtu.edu.cn

Received Date: 16 August 2024
Revised Date: 7 November 2024
Accepted Date: 19 November 2024
Available Online: 20 November 2024

Figures(5)

The electrochemical nitric oxide reduction reaction (NORR) to NH₃ represents a promising avenue for NO removal and NH₃ synthesis. It is essential to develop catalysts with superior performance for this process. We systematically studied a series of single-atom alloy catalysts (SAACs) with Pd single-atom dopants using density functional theory (DFT) calculations and machine learning (ML). Based on the energetic span model, we take G_max(η) as a descriptor to evaluate the reaction activity of SAACs. After comprehensively considering the stability, activity, and NH₃ selectivity of SAACs, Cu and Pd/Cu SAAC are screened out as candidate NORR to NH₃ catalysts. To predict the G_max(η) descriptor, the extreme gradient boosting regression (XGBR) ML algorithm was adopted with geometric/electronic properties of the SAACs as input features. Additionally, we proposed a mathematical formula to correlate the crucial features and the G_max(η) descriptor using the sure independence screening and sparsifying operator (SISSO) approach. This work provides an understanding of the complex NORR mechanisms and offers a strategy to rationally design highly efficient SAACs.
- Nitric oxide reduction reaction,
- Single-atom alloy catalysts,
- Ammonia synthesis,
- DFT calculation,
- Machine learning
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沈阳化工大学材料科学与工程学院沈阳 110142

Understanding the activity origin of Pd-anchored single-atom alloy catalysts for NO-to-NH3 conversion by DFT studies and machine learning

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通讯作者: 陈斌, bchen63@163.com

Understanding the activity origin of Pd-anchored single-atom alloy catalysts for NO-to-NH₃ conversion by DFT studies and machine learning