Prediction of SF₆ Replacement Gases Based on Machine Learning.

ABSTRACT: Global Warming Potential (GWP) is a crucial criterion for assessing the impact of SF₆ replacement gases on climate change, which is determined by radiative efficiency (RE) and atmospheric lifetime (τ). This work leverages six modified machine learning (ML) methods to estimate GWP and its critical parameters (τ and RE), as well as explore their correlations via molecular descriptors, aiming at high-throughput screening and evaluation of potential SF₆ replacement gases. Based on the coefficient of determination, mean absolute error, and root-mean-square error, the optimal ML methods are Histogram Gradient Boosting Regression for GWP (0.95, 0.28, and 0.33), Gradient Boosting Regression for τ (0.92, 0.28, and 0.40), and Extreme Tree for RE (0.90, 0.04, and 0.05). Furtherm

SUBMITTER: Ding G 

PROVIDER: S-EPMC12547788 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature