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 GWP 100 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 GWP 100 . 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 GWP 100 ) 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 SF 6 replacement gas for the electric industry and identified 84 potential candidates.

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC A , the virtual special issue papers span applications to all aspects of chemical dynamics, molecular property prediction, and electronic structure. A large number of contributions within this collection in JPC A address fundamental research into new or adaptation of existing models for applications of ML to physical chemistry spanning many topical areas. − Many of the papers relate to using ML/AI and other data-driven models to enhance methods within physical chemistry. − A number of contributions address the creation or analysis of ground and excited state potential energy surfaces, − while others address dynamics, kinetics, and thermochemistry, a major area of interest within JPC A . − The use of ML to improve accuracy and efficiency in calculation of molecular properties is also addressed in many articles. − …”