ArticleScientific reports2025
Artificial intelligence-empowered functional design of semi-transparent optoelectronic and photonic devices via deep Q-learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- AI-enhanced inverse design of photonic crystal fiber optical modulator using deep reinforcement learning technique.Scientific reports · 2026Article
- Engineering of Optoelectronic Devices for Renewable Energy Applications.Micromachines · 2026Review
- Emerging Trends in Thermo-Optic and Electro-Optic Materials for Tunable Photonic Devices.Materials (Basel, Switzerland) · 2025Review
- A novel efferocytosis-related gene signature for predicting prognosis and therapeutic response in bladder cancer.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photonic-based design of semi-transparent organic solar cells (ST-OSCs) demands a careful balance between optical transparency and photovoltaic efficiency, often requiring trade-offs that complicate optimization. This study, for the first time, employs deep Q-learning, a reinforcement learning algorithm, to address this challenge, integrating transfer matrix method for precise optical calculations. The proposed framework optimizes asymmetric dielectric/metal/dielectric photonic-based transparent contact systems combined with novel PBDB-T:ITIC-based active layers, achieving superior optical and photovoltaic performance. The deep Q-learning algorithm successfully identified configurations yielding a maximum photo-current density (J
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Registered trials
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