High-Throughput Screening of Gas Sensor Materials for Decomposition Products of Eco-Friendly Insulation Medium by Machine Learning.

Nowadays, trifluoromethyl sulfonyl fluoride (CFSOF) has shown great potential to replace SF as an eco-friendly insulation medium in the power industry. In this work, an effective and low-cost design strategy toward ideal gas sensors for the decomposed gas products of CFSOF was proposed. The strategy achieved high-throughput screening from a large candidate space based on first-principle calculation and machine learning (ML). The candidate space is made up of different transition metal-embedded graphic carbon nitrides (TM/g-CN) owing to their high surface area and subtle electronic structure. Four main noteworthy decomposition gases of CFSOF, namely, CF, SO, SOF, and HF, as well as their initial stable structure on TM/g-CN were determined. The best-performing ML model was established and implemented to predict the interaction strength between gas products and TM/g-CN, thus determining the promising gas-sensing materials for target gases with the requirements of interaction strength, recovery time, sensitivity, and selectivity. Further analysis guarantees their stability and reveals the origin of excellent properties as a gas sensor. The high-throughput strategy opens a new avenue of rational and low-cost design principles of desirable gas-sensing materials in an interdisciplinary view.