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- Title
AI-driven electro chromic materials and devices for nanofabrication in machine learning integrated environments.
- Authors
Prasanna, K. Mahesh; Shukla, Aasheesh; Tamizharasu, K.; Ganatra, Amit; Shelke, Atmaram; Metwally, Ahmed Sayed M.; Aftab, Sikandar
- Abstract
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.
- Subjects
CHROMIC materials; MACHINE learning; NANOFABRICATION; ELECTROCHROMIC substances; ELECTROCHROMIC devices; ELECTROCHROMIC windows
- Publication
Optical & Quantum Electronics, 2024, Vol 56, Issue 1, p1
- ISSN
0306-8919
- Publication type
Article
- DOI
10.1007/s11082-023-05656-1