상세 보기
- Yang, Yuejiao;
- Hu, Xiaopei;
- Lv, Yipin;
- Ma, Rongwei;
- Wei, Xinru;
- ... Lee, Jin Yong;
- 외 2명
WEB OF SCIENCE
1SCOPUS
1초록
The oxygen reduction reaction (ORR) is critical for sustainable energy solutions, yet noble metal catalysts' costs limit their scalability. This study investigates transition metal-doped biphenylene network (TM-BPNs) single-atom catalysts (SACs) with tailored nitrogen doping as affordable alternatives. Using density functional theory (DFT), we designed 460 TM-BPNs variants with 3d metals (Sc-Zn), evaluating their structures, electronic properties, and dual stability. Most TM-BPNs displayed quasi-metallic or semiconducting traits and robust thermodynamic and electrochemical stability, indicating synthetic viability. ORR assessments showed high potential, with V5/CCCC-Ni achieving an ultralow overpotential of 0.13 V. A novel approach combining the Extreme Gradient Boosting Regressor (XGBR) and Sure Independence Screening and Sparsifying Operator (SISSO) was developed to predict ORR performance. XGBR, with an R 2 of 0.96, identified key features such as the atomic number of TM (NA) and coordination environment influencing Delta G *OH, validated by SHAP analysis. SISSO then derived a 3D descriptor (R 2 = 0.89) that elucidates physical properties governing catalysis, enhancing interpretability. This XGBR-SISSO synergy enables rapid screening and mechanistic insight, underscoring N-doping's role in optimizing TM-BPNs. These findings provide a versatile framework for designing efficient, low-cost ORR electrocatalysts.
키워드
- 제목
- Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy
- 저자
- Yang, Yuejiao; Hu, Xiaopei; Lv, Yipin; Ma, Rongwei; Wei, Xinru; Kim, Hyun Woo; Lee, Jin Yong; Kang, Baotao
- 발행일
- 2026-01-08
- 유형
- Article
- 권
- 130
- 호
- 1
- 페이지
- 82 ~ 92