Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

Abstract

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth's infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule's atmospheric lifetime and GWP₁₀₀ based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP₁₀₀. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP₁₀₀) compared with 0.535 (Atkinson's method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF₆ replacement gas for the electric industry and identified 84 potential candidates.