RETRACTED ARTICLE: AI-driven electro chromic materials and devices for nanofabrication in machine learning integrated environments

• Published in: Optical and quantum electronics Vol. 56; no. 1
• Main Authors: Prasanna, K. Mahesh, Shukla, Aasheesh, Tamizharasu, K., Ganatra, Amit, Shelke, Atmaram, Metwally, Ahmed Sayed M., Aftab, Sikandar
• Format: Journal Article
• Language: English
• Published: New York Springer US 2024; Springer Nature B.V
• Subjects: Algorithms; Characterization and Evaluation of Materials; Computer Communication Networks; Design optimization; Electric fields; Electrical Engineering; Electrochromic cells; Electrochromism; Lasers; Light modulation; Machine learning; Nanofabrication; Optical Devices; Optical properties; Optics; Photonics; Physics; Physics and Astronomy; Electrochromic devices; AI-driven materials; Nanofabrication; Machine learning integration; Optical modulation; Responsive interfaces
• ISSN: 0306-8919; 1572-817X
• DOI: 10.1007/s11082-023-05656-1

This study looks into the introduction of AI-driven electrochromic materials and devices into nanofabrication methods for use in ML-integrated environments. When exposed to an electric field, electrochromic materials experience reversible changes in optical properties due to dynamic optical modulation. Because of developments in AI-assisted design, optimization, and fabrication, advanced electrochromic devices with improved performance are now conceivable. The incorporation of AI-optimized electrochromic materials into nanofabrication operations and their application in ML-integrated systems are described, as well as their synthesis and characterization. Several test datasets revealed that the AI-driven strategy improved OME, Response Times, CE, and EE. These findings validate the importance of applying AI algorithms to guide material design, optimize production, and enable real-time adaptation for greater optical modulation and energy efficiency.