Computational Exploration and Experimental Verification for Designing SF6 Alternatives

There have been numerous experimental efforts in developing SF6 alternatives. However, promising candidates have not been identified due to the tradeoff relationship among multiple requirements necessary for eco-friendly insulating gases. This study presents a computational molecular exploration and experimental verification for identifying potential SF6 alternatives. We propose machine learning models based on quantum mechanical insights to realize extrapolative prediction of gas properties. The evaluation results demonstrate the superior reliability of the proposed models compared with the existing ones, while also achieving high-throughput screening. The molecular exploration is systematically conducted to narrow down candidates that pose low global warming potential (GWP) and superior insulation strength under low-temperature and high-pressure conditions. The screening results reveal new candidates with enhanced insulation performance and favorable environmental impact compared with the existing candidates, including C4F7N, C5F10O, and CF3I. Furthermore, we newly measure the breakdown strength of a gas material for which no experimental data are available, and whose structure is extrapolative relative to the training data. The experimental result matches the prediction well, serving as an example that indicates the extrapolative capability of the proposed model. Our methodology and exploration results contribute to a wide range of material design as well as SF6 alternatives.