MIR-QBIC: A High-Q All-Dielectric Metasurface Benchmark for Mid-Infrared Biosensing

Deep learning is advancing nanophotonic device design, yet learning the sharp, high-quality-factor (Q) resonances relevant to molecular sensing remains constrained by data: these features are computationally demanding to resolve and annotate, and public datasets capturing them remain scarce. We introduce MIR-QBIC, an open dataset of 13,741 rigorous coupled-wave analysis (RCWA) simulations of Ge/CaF2 quasi-bound-state-in-the-continuum (quasi-BIC) metasurfaces operating over 5-8 um. Each structure is described by four interpretable geometric parameters and paired with broadband and adaptively resolved transmission spectra, together with resonance wavelength, radiative quality factor, dip depth, and Fano descriptors. The responses span a broad linewidth range with a sparse high-Q tail, and a substantial fraction of resonances lie in the amide-I region motivating mid-infrared biosensing. Numerical verification, frozen quality-factor-stratified splits, and a physics-aware evaluation protocol support systematic study of scalar prediction, adaptive-window reconstruction, and high-Q generalization. Six classical and neural baselines demonstrate the benchmark's utility and difficulty: similar quality-factor accuracy can accompany substantially different adaptive-window errors, while high-Q training coverage improves generalization without resolving the sharpest responses. MIR-QBIC provides a shared, physically grounded resource for advancing narrow-feature, high-dynamic-range spectral learning.