A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells

A library of metal oxide-conjugated polymer composites was prepared, encompassing WO₃ -polyaniline (PANI), WO₃ -poly(N-methylaniline) (PMANI), WO₃ -poly(2-fluoroaniline) (PFANI), WO₃ -polythiophene (PTh), WO₃ -polyfuran (PFu) and WO₃ -poly(3,4-ethylenedioxythiophene) (PEDOT) which were used as hole selective layers for perovskite solar cells (PSCs) fabrication. We adopted machine learning approaches to predict and compare PSCs performances with the developed WO₃ and its composites. For the evaluation of PSCs performance, a decision tree model that returns 0.9656 R² score is ideal for the WO₃ -PEDOT composite, while a random forest model was found to be suitable for WO₃ -PMANI, WO₃ -PFANI, and WO₃ -PFu with R² scores of 0.9976, 0.9968, and 0.9772 respectively. In the case of WO₃ , WO₃ -PANI, and WO₃ -PTh, a K-Nearest Neighbors model was found suitable with R² scores of 0.9975, 0.9916, and 0.9969 respectively. Machine learning can be a pioneering prediction model for the PSCs performance and its validation.

Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices

This study shows that the model fitting based on machine learning (ML) from experimental data can successfully predict the electrochromic characteristics of single- and dual-type flexible electrochromic devices (ECDs) by using tungsten trioxide (WO3) and WO3/vanadium pentoxide (V2O5), respectively. Seven different regression methods were used for experimental observations, which belong to single and dual ECDs where 80% percent was used as training data and the remaining was taken as testing data. Among the seven different regression methods, K-nearest neighbor (KNN) achieves the best results with higher coefficient of determination (R 2) score and lower root-mean-squared error (RMSE) for the bleaching state of ECDs. Furthermore, higher R 2 score and lower RMSE for the coloration state of ECDs were achieved with Gaussian process regressor. The robustness result of the ML modeling demonstrates the reliability of prediction outcomes. These results can be proposed as promising models for different energy-saving flexible electronic systems.