Dielectric laser accelerators (DLAs), as compact particle accelerators, rely critically on their structural design to determine both the energy gain and beam quality of accelerated bunches. While most existing DLAs are driven by near-infrared lasers at ~1 μm wavelength, employing long-wave infrared (LWIR) lasers at ten times that wavelength offers the potential for superior beam quality without sacrificing acceleration gradient. To address the lack of optimized structural designs in the LWIR band where long-distance acceleration poses unique challenges—we introduce a deep learning–based design methodology for LWIR dielectric grating accelerator structures. Our approach integrates geometric parameters, material properties, and optical-field energy metrics into a unified evaluation framework and uses a surrogate model to predict particle energy gain with high precision. Optimal structural parameters are then extracted to realize the final design. Simulation results show an energy gain of 99.5 keV (a 19.9% year-over-year improvement), 100% transmission efficiency, a beam spot radius of 14.5 μm, and an average beam current of 20.4 fA—6.9 fold higher than comparable near-infrared gratings—while maintaining equivalent beam brightness. This work provides a viable technical route for high-net-gain LWIR dielectric grating accelerators and offers a novel framework for the structural optimization of complex optoelectronic devices.
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