Advanced Empirical Prediction of Electric Breakdown Field and Boiling Point of Gases using Machine Learning Techniques and Quantum Chemical Calculations

Abstract

This study demonstrates that the accuracy of empirical prediction of electric breakdown field of gases can be improved by adopting an appropriate machine learning approach. The performance of the machine learning models for predicting electrical breakdown strengths were evaluated by means of double cross validation technique. It is shown that the coefficient of determination between experimental and predicted electric breakdown strengths can be increased by roughly 30% and the standard deviation can be decreased by roughly 30% by adopting kernel ridge regression (KRR) method and by choosing the best number and combinations of predictors. Electric breakdown strengths and boiling points of (CF₃)₂CFCN and CF₃C(O)CF(CF₃)₂ molecules that are recently proposed as alternative gases for SF₆, are predicted by KRR method with the aid of quantum chemical calculations. Predicted electric breakdown strengths and boiling points were in good agreement with experimental findings; the prediction errors of breakdown strengths and boiling points were within 30 and 10%, respectively.