A NEAT Approach to Evolving Neural-Network-based Optimization of Chiral Photonic Metasurfaces: Application of a Neuro-Evolution Pipeline
The design of chiral metasurfaces with tailored optical properties remains a central challenge in nanophotonics due to the highly nonlinear relationship between geometry and chiroptical response. Machine-learning-assisted optimization pipelines have recently emerged as efficient tools to accelerate this process, yet their performance strongly depends on the choice of neural-network (NN) architecture. In this work, we integrate the NeuroEvolution of Augmenting Topologies (NEAT) algorithm into an established deep-learning optimization framework for dielectric chiral metasurfaces. NEAT autonomously evolves both network topology and connection weights, enabling task-specific architectures without manual tuning, whereas the reinforcement-learning strategy in our framework evolves knowledge of the solution space and fine-tunes a model's weights in parallel. Using a pipeline-produced dataset of 9,600 simulated GaP metasurface geometries, we evaluate NEAT under varying input dimensionalities, feature-scaling methods, and data sizes. With standardized feature scaling yielding the most consistent performance for both examined output dimensionalities, the relatively compact NEAT-evolved NN models, when integrated into the full optimization pipeline, achieve similar or improved predictive accuracy and generalization compared to initially employed dense few-layer perceptrons. Accordingly, these resource-efficient models successfully perform inference of metasurfaces exhibiting strong circular dichroism in the visible spectrum, allowing for transfer learning between simulated and experimental data. This approach demonstrates a scalable path toward adaptive, self-configuring machine-learning frameworks for automated photonic design both standalone and as building block for agentic artificial intelligence (AI).