Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms
Bocheng Jiang,
Honglong Ning,
Muyun Li,
Rihui Yao,
Chenxiao Guo,
Yucheng Huang,
Zijie Guo,
Dongxiang Luo,
Dong Yuan,
Junbiao Peng
Affiliations
Bocheng Jiang
Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials, State Key Laboratory of Luminescent Materials and Devices, School of Materials Sciences and Engineering, South China University of Technology, Guangzhou 510640, China
Honglong Ning
Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials, State Key Laboratory of Luminescent Materials and Devices, School of Materials Sciences and Engineering, South China University of Technology, Guangzhou 510640, China
Muyun Li
Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials, State Key Laboratory of Luminescent Materials and Devices, School of Materials Sciences and Engineering, South China University of Technology, Guangzhou 510640, China
Rihui Yao
Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials, State Key Laboratory of Luminescent Materials and Devices, School of Materials Sciences and Engineering, South China University of Technology, Guangzhou 510640, China
Chenxiao Guo
Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials, State Key Laboratory of Luminescent Materials and Devices, School of Materials Sciences and Engineering, South China University of Technology, Guangzhou 510640, China
Yucheng Huang
Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials, State Key Laboratory of Luminescent Materials and Devices, School of Materials Sciences and Engineering, South China University of Technology, Guangzhou 510640, China
Zijie Guo
Department of Mechanical Engineering, University of Southampton, Southampton SO17 1BJ, United Kingdom
Dongxiang Luo
Huangpu Hydrogen Innovation Center/Guangzhou Key Laboratory for Clean Energy and Materials, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Dong Yuan
Guangdong Provincial Key Laboratory of Optical Information Materials and Technology and Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People’s Republic of China
Junbiao Peng
Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials, State Key Laboratory of Luminescent Materials and Devices, School of Materials Sciences and Engineering, South China University of Technology, Guangzhou 510640, China
This research explores the application of machine learning (ML) in the domain of electrochromic (EC) technology, focusing specifically on liquid-state electrochromic devices (ECDs). Unlike traditional solid-state ECDs, liquid devices offer a simpler structure, reducing manufacturing variables and potentially improving prediction accuracy with minimal input data. Two types of ECDs were developed using solutions of ammonium metatungstate-iron(II) chloride and ammonium metatungstate-iron(II) sulfate, resulting in 20 different devices with varying concentration gradients. Transmittance alterations under different current densities were measured to determine modulation range and time response, serving as training data for ML models. Seven regression models were employed to construct EC models and predict optimal device solutions. Subsequent manufacturing and testing of new ECDs validated the predictions, with a comparative analysis of EC characteristics and model fitting performance conducted between the two types of ECDs. For ammonium metatungstate-iron(II) chloride ECDs, under a 5 mA applied current, the maximum optical modulation reached 23.67%, with a coloration efficiency of 17.54 cm2/C (under 700 nm). For ammonium metatungstate-iron(II) sulfate ECDs, under a 5 mA applied current, the maximum optical modulation reached 18.92%, with a coloration efficiency of 17.05 cm2/C (under 700 nm). The coloring time (tc) and bleaching time (tb) for ammonium metatungstate-iron(II) chloride ECDs were ∼14 and 8 s, respectively. The predicted maximum optical modulation for ammonium metatungstate-iron(II) chloride and ammonium metatungstate-iron(II) sulfate ECDs were 23.67% and 18.92%, respectively, with prediction accuracies reaching 97.90% and 96.97%, respectively. Decision tree regression (DTR) and kernel ridge regression (KRR) emerged as the most effective ML methods for these ECDs.
