Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy
- Type
- Article
- Citation
- JOURNAL OF PHYSICAL CHEMISTRY C, v.130, no.1, pp.82 - 92
- Issued Date
- 2026-01
- Abstract
- 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.
- Publisher
- AMER CHEMICAL SOC
- ISSN
- 1932-7447
- Appears in Collections:
- Department of Chemistry > 1. Journal Articles
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