A ML-Driven Study on Feature Descriptors and Mechanisms for Adsorption of Air Discharge Decomposition Components

Insulation materials in air switchgear may degrade due to partial discharge (PD). Characteristic gases generated by PD (such as CO, NO, and NO₂) can be detected by gas sensors. However, existing gas-sensitive material screening relies on trial-and-error methods or costly density functional theory (DFT) calculations. Machine learning (ML)-driven DFT calculations have emerged as a new paradigm in gas-sensitive material research. This approach significantly accelerates the rational design of high-performance gas-sensitive materials by efficiently predicting adsorption behavior and revealing underlying microscopic mechanisms. This study screened 14 key features from 200 sets of adsorption data across 50 literature sources (2018–2024), then compared and optimized six ML models (XGBoost, GBR, etc.) to evaluate the predictive performance of D and Ead. Subsequently, SHAP and PDPs analyses were conducted to quantify feature contributions and identify core factors. Research findings indicate that XGBoost achieved prediction R² values of 91.55% for adsorption distance and 89.79% for adsorption energy, significantly outperforming other models (GBR ranked second with R² values of 89.79% and 83.56%, respectively). In terms of generalization ability, XGBoost outperforms GBR in extreme value usage and feature balancing utilization, which is overly dependent on substrate material features. Charge transfer (Qₜ) and dopant atom density (ρd) are the core factors affecting the adsorption behavior, and the SHAP analysis shows that Qₜ has the highest contribution to the model output. Substrate and dopant characteristics contribute equally in the XGBoost model (∼45% each), while the GBR model is overly dependent on substrate characteristics (>60%).