Advanced Materials for Intelligent Nanophotonics: AI-Driven Reconfigurable Metasurfaces in Nonlinear and Quantum Regimes
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Optical and Photonic Materials".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 36
Special Issue Editors
Interests: nanophotonics; artificial intelligence; nonlinear optics; quantum optics; reconfigurable metasurfaces; PINN
Special Issue Information
Dear Colleagues,
The extraordinary optical properties of nanophotonic materials, such as extreme light confinement, nonlinear enhancement, and quantum emission, have been theoretically established for decades. Certain applications, such as dielectric metasurfaces for linear optics and AI-optimized photonic crystals for telecommunications, have already been commercialized through mature nanofabrication techniques.
Conversely, more complex capabilities remain underutilized in practical devices despite their theoretical promise. Nonlinear optical phenomena at the nanoscale, quantum light sources with engineered statistics, and dynamically reconfigurable metasurfaces often fail to exit research laboratories due to prohibitive design complexities and insufficient control over nanomaterial properties.
Recently, the integration of artificial intelligence with nanophotonics has fundamentally transformed this landscape. Physics-Informed Neural Networks (PINNs) now enable inverse design of nanostructures, while deep learning frameworks predict optical responses of complex meta-atoms with unprecedented accuracy. Concrete breakthroughs include deep reinforcement learning-designed reconfigurable metasurfaces with electrically tunable nonlinearities, PINN-optimized 2D materials for high-harmonic generation, and graph neural networks predicting quantum emitter interactions in diamond nanostructures. Moreover, these applications extend to inverse-designed photonic quantum gates, AI-optimized topological lasers, and intelligent Mie-resonator arrays.
Critically, these AI methodologies have demonstrated an unprecedented capability to manipulate light–matter interactions at subwavelength scales, enabling functionalities once considered impractical. Examples include differentiable programming of metasurfaces for quantum state engineering, neural network-controlled phase-change materials for nonvolatile reconfigurability, and multi-objective deep learning frameworks co-optimizing nonlinear efficiency and quantum decoherence in nanoresonators.
This Special Issue aims to comprehensively review recent advancements in AI-driven nanomaterial design for nonlinear and quantum nanophotonics. We welcome all contributions including full papers, communications, and reviews.
Dr. Omar A.M. Abdelraouf
Dr. Mohamed Ragab
Guest Editors
Manuscript Submission Information
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Keywords
- nanophotonics
- artificial intelligence
- nonlinear optics
- quantum optics
- reconfigurable metasurfaces
- PINN
- deep learning
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