Machine Learning Test for Complementary Electrochromic Device

In this study, we use machine learning (ML) model can predict the electrochromic characteristic of electrochromic devices (ECDs) using by preparing tungsten trioxide (WO
3
) film as the working electrode and iridium oxide (IrO
2
) film as the counter electrode for fitting experimental data. The purpose of this study is to put the predicted transmittance value from the ML method with K-nearest neighbor (KNN) and Gaussian process regressor (GPR) approaches to predict ECDs parameters such as intercalation/extraction charge density, and the transmittance of the coloration/bleaching ECDs.