Boiling Temperature Prediction Model of Environmental Friendly Insulation Molecules Based on Machine Learning

With the increasing global awareness of environmental protection, the research and development of environmental protection materials has become a hot spot in various fields. In the electrical industry, insulating materials play a vital role, however, the traditional insulating material sulfur hexafluoride has a strong greenhouse effect. Therefore, the development of environmentally friendly insulation materials has become an urgent need. The boiling temperature is one of the important parameters to evaluate the properties of insulating materials, and its accurate prediction is very important for the screening and optimization of materials. In recent years, the rapid development of machine learning technology provides a new idea for boiling temperature prediction. This paper aims to build a machine learning-based boiling temperature prediction model for environmentally friendly insulation molecules in order to improve the prediction accuracy and promote the research and development of environmentally friendly insulation materials.