Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms
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 po...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-02-01
|
| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0247775 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850027065662767104 |
|---|---|
| author | Bocheng Jiang Honglong Ning Muyun Li Rihui Yao Chenxiao Guo Yucheng Huang Zijie Guo Dongxiang Luo Dong Yuan Junbiao Peng |
| author_facet | Bocheng Jiang Honglong Ning Muyun Li Rihui Yao Chenxiao Guo Yucheng Huang Zijie Guo Dongxiang Luo Dong Yuan Junbiao Peng |
| author_sort | Bocheng Jiang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dac4b87c1da742e48cca780e5abd3756 |
| institution | DOAJ |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-dac4b87c1da742e48cca780e5abd37562025-08-20T03:00:21ZengAIP Publishing LLCAIP Advances2158-32262025-02-01152025308025308-1010.1063/5.0247775Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithmsBocheng Jiang0Honglong Ning1Muyun Li2Rihui Yao3Chenxiao Guo4Yucheng Huang5Zijie Guo6Dongxiang Luo7Dong Yuan8Junbiao Peng9Guangdong 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, ChinaGuangdong 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, ChinaGuangdong 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, ChinaGuangdong 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, ChinaGuangdong 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, ChinaGuangdong 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, ChinaDepartment of Mechanical Engineering, University of Southampton, Southampton SO17 1BJ, United KingdomHuangpu Hydrogen Innovation Center/Guangzhou Key Laboratory for Clean Energy and Materials, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangdong 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 ChinaGuangdong 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, ChinaThis 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.http://dx.doi.org/10.1063/5.0247775 |
| spellingShingle | Bocheng Jiang Honglong Ning Muyun Li Rihui Yao Chenxiao Guo Yucheng Huang Zijie Guo Dongxiang Luo Dong Yuan Junbiao Peng Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms AIP Advances |
| title | Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms |
| title_full | Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms |
| title_fullStr | Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms |
| title_full_unstemmed | Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms |
| title_short | Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms |
| title_sort | predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms |
| url | http://dx.doi.org/10.1063/5.0247775 |
| work_keys_str_mv | AT bochengjiang predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT honglongning predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT muyunli predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT rihuiyao predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT chenxiaoguo predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT yuchenghuang predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT zijieguo predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT dongxiangluo predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT dongyuan predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms AT junbiaopeng predictivemodelingofelectrochromicperformanceinammoniummetatungstatesolutionsusingmachinelearningalgorithms |