A machine learning feature descriptor approach: Revealing potential adsorption mechanisms for SF decomposition product gas-sensitive materials.

The man-made gas sulfur hexafluoride (SF) is an excellent and stable insulating medium. However, some insulation defects can cause SF to decompose, threatening the safe operation of power grids. Based on this, it is of great significance to find and effectively control the decomposition products of SF in time. Gas sensors have proven to be an effective way to detect these decomposition gases (SO, SOF, SOF, HS, and HF). Nanomaterials with gas-sensitive properties are at the heart of gas sensors. In recent years, data-driven machine learning (ML) has been widely used to predict material properties and discover new materials. However, it has become a major challenge to establish a common model between material properties derived from various types of calculations and intelligent algorithms. In order to make some progress in addressing this challenge. In this work, 250 data sets were extracted from 52 publications exploring the detection of SF decomposition products by nanocomposites based on relevant work over the past 10 years, and the adsorption behavior of SF decomposition products can be predictively analyzed. By comparing six different algorithmic models, the best model for predicting the adsorption distance (XGBoost: R = 91.94 %) and adsorption energy (GBR: R = 78.63 %) of SF decomposed gas was identified. Subsequently, the importance of each of the selected feature descriptors in predicting the gas adsorption effect was explained. This work combines first-principles computational results and machine-learning algorithms with each other to provide a new research idea for evaluating the gas sensing capability of nanocomposites.