Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy
  • Yang, Yuejiao
  • Hu, Xiaopei
  • Lv, Yipin
  • Ma, Rongwei
  • Wei, Xinru
  • ... Lee, Jin Yong
  • 외 2명
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초록

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

REDUCTIONPERFORMANCEALLOTROPENETWORK
제목
Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy
저자
Yang, YuejiaoHu, XiaopeiLv, YipinMa, RongweiWei, XinruKim, Hyun WooLee, Jin YongKang, Baotao
DOI
10.1021/acs.jpcc.5c06757
발행일
2026-01-08
유형
Article
저널명
Journal of Physical Chemistry C
130
1
페이지
82 ~ 92