Deep Learning-Empowered Inverse Design of Metasurfaces for Tailored Superchiral Fields and Enhanced Chiral Sensing

Metasurfaces supporting superchiral fields provide an effective route for amplifying the intrinsically weak circular dichroism (CD) response of chiral molecules, but their design usually relies on inefficient trial-and-error optimization. Here, we propose an automated inverse-design framework that integrates a deep neural network (DNN) with Bayesian optimization (BO) for the on-demand design of metasurfaces with tailored superchiral-field responses. A high-accuracy DNN surrogate model is first trained to establish the mapping between structural parameters and eigenmodes, while a BO module performs inverse optimization through a physics-informed weighted composite objective function. To address the mode-label switching problem in modal prediction, the task is reformulated as a modal-set prediction problem and optimized using a permutation-invariant loss, which markedly improves training stability and prediction accuracy. Based on this framework, dual high-Q mode degeneracy at target wavelengths is rapidly achieved under a fixed lattice period, enabling efficient generation of customized superchiral fields. Compared with expert-guided parametric scanning, the design speed is improved by approximately 5145 times. The optimized metasurfaces provide up to 6000-fold optical chirality enhancement and yield a CD response enhanced by nearly three orders of magnitude compared to the reference without metasurfaces. This framework provides an efficient and generalizable route toward intelligent inverse design of chiral photonic devices for ultrasensitive sensing.