NanoPhotoNet-Lase: Physics-Informed Neural Networks for Accelerated Design of Coherent Metasurface Nanolasers

The integration of artificial intelligence with physical modeling offers a transformative route for accelerating the design of active nanophotonic devices. Here, we present NanoPhotoNet-Lase, a physics-informed neural network (PINN) framework that embeds the electromagnetic and rate equations of lasing directly into its learning process to expedite the design of metasurface nanolasers. By coupling Maxwell's vector Helmholtz equation with the four-level population dynamics of dye gain media, the model achieves physics-guided prediction of optical responses, enabling rapid estimation of lasing thresholds across arbitrary nanostructure geometries and material configurations. Using a high-index metasurface cavity, the NanoPhotoNet-Lase model identifies optimized geometries supporting quasi-bound states in the continuum (BICs) with strong confinement and high-quality factors. The predicted lasing was experimentally realized using Rhodamine B dye as the gain medium. The measured lasing threshold (Pth~565 µJ/cm²) and emission wavelength (λ∼620 nm) exhibited <1% deviation from model predictions. Importantly, the framework enables the design of phase-gradient nanolaser metalenses and beam steering that numerically demonstrated coherent, directional, focused or steered emission. This work bridges physics-informed machine learning with experimental nanophotonics, establishing a scalable paradigm for real-time, physically interpretable design of coherent light-emitting metasurfaces.