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.

中文翻译：

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利用机器学习预测 SF6 替代气体的大气寿命和全球变暖潜力

全球变暖潜势 (GWP) 是分子将地球红外辐射捕获为热量能力的相对量度。分子 GWP 的测量或预测基于两个因素：分子的辐射效率和大气寿命。虽然使用计算化学方法（例如密度泛函理论（DFT））计算分子的辐射效率是成熟且可靠的，但迄今为止，估计大气寿命的计算方法的开发仍然具有挑战性且受到限制。在这篇文章中，我们开发了一种机器学习 (ML) 方法来根据电子和几何特征来估计分子的大气寿命和 GWP ₁₀₀ 。我们使用开发的 ML 模型对最先进的计算工作流程进行了基准测试，以估计大气寿命和 GWP ₁₀₀。所开发的 ML 模型优于现有方法，平均绝对误差值为 0.234（ML 预测的大气寿命）和 0.249（GWP ₁₀₀的直接 ML 模型），而之前的方法为 0.535（阿特金森方法）和 0.773（Kazakov 等人）作品。开发的模型用于筛选 PubChem 和 bigQM7 数据集中的超过 7000 个分子，为电力行业寻找 SF _{6替代气体，并确定了 84 种潜在候选气体。}