Accelerating Discovery of WX ₂ -Based Gas Sensors (X = S, Se, and Te) for C ₄ F ₇ N Decomposed Species via Density Functional Theory and Machine Learning

C 4 F 7 N decomposition products such as HF, CO, C 2 F 3 N and C 2 F 4 pose serious risks to the operation status of C 4 F 7 N-based insulation devices in the electrical system. This method, which combines machine learning (ML) with density functional theory (DFT) calculations, proposes a novel and efficient method for accelerating screening of transition metal (TM)-doped WX 2 (X = S, Se, and Te) as potential sensing materials for such four typical gases. By establishing input feature descriptors and conducting training and optimization of eight machine learning models, the optimal models for predicting two crucial adsorption and sensing parameters, namely, adsorption energy ( E ads ) and bandgap modulation (Δ B g ) are determined with high accuracy. In addition, the sensing response of the selected 8 materials is further analyzed to illustrate their potential for sensing typical gas species. This work not only accelerates the discovery of WX 2 -based sensing materials upon C 4 F 7 N decomposed species but also lays a foundation for the rational design of advanced gas sensors, typically realizing the insulation evaluation in the power system.