MOCLIP (Metasurface Optics Contrastive Learning Pretrained) is a nanophotonic foundation model that unifies metasurface geometry and spectra in a shared latent space using contrastive learning on an ImageNet‑1K–sized experimental dataset. It enables high‑throughput zero‑shot inverse design, predicting 0.2 million samples per second and allowing the design of a full 4‑inch wafer of high‑density metasurfaces in minutes. The model also supports generative latent‑space optimization with 97 % accuracy and demonstrates an optical information storage concept achieving 0.1 Gbit/mm², six times higher than commercial optical media.
By S. Rodionov, A. Burguete-Lopez, M. Makarenko, Q. Wang, F. Getman, A. Fratalocchi
arXiv:2608. 11860v1 Announce Type: cross Abstract: Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping remains challenging due to non-uniqueness and fine geometric features.
By Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Shujaat Khan
The paper proposes using ultrathin metalenses to physically encode metric depth cues into two polarized optical wavefronts, enabling accurate monocular depth estimation. By aligning a pretrained depth foundation model with these optical signals through direct fine‑tuning, and by creating a simulation pipeline to generate realistic metalens responses from RGB‑D data, the authors bridge the gap between nanophotonics and learned depth priors. Experiments show that this method surpasses conventional monocular metric depth estimation and depth‑from‑defocus baselines.
By Bingxuan Li, Jiahao Wu, Yuan Xu, Zezheng Zhu, Yunxiang Zhang, Kenneth Chen, Yanqi Liang, Nanfang Yu, Qi Sun
The paper presents a 2 cm diameter silicon hyperboloid metalens that captures broadband mid‑wave infrared (MWIR, 3–5 µm) intensity information. By combining this simple metasurface with a computational reconstruction model trained on only four image captures, the authors achieve imaging across the entire MWIR band, overcoming the typical chromatic dispersion limitations of meta‑optics. This approach eliminates the need for complex, achromatic metalens designs, offering a compact and potentially low‑cost solution for MWIR imaging systems.
By Rose Johnson, Debabrata Mandal, Zihao Zou, Jessie Wei, Anna Wirth-Singh, Vladimir Yarmolik, Johannes Froech, Arka Majumdar, Praneeth Chakravarthula
The paper presents a method for training single‑step neural surrogates that can handle wave‑scattering inverse problems with tens of thousands of controllable variables. By dynamically generating training examples through gradient ascent and using a replay dataset with normalization, the authors achieve a surrogate that accurately models two‑dimensional wave scattering for up to 41,772 variables and can generalize to over 3 million variables without retraining. The surrogate demonstrates comparable or better performance than traditional FDTD simulations for large‑scale forward simulations and inverse design of photonic devices, achieving speedups up to 26.5×.
The paper introduces a unified large language model workflow for modeling and inverse-design of metasurfaces across multiple families. By converting geometries, design parameters, and optical responses into a shared instruction‑following text format, the authors fine‑tune Gemma‑2‑9B on eight distinct metasurface families. Compared to single‑family models, the joint model predicts all families’ optical responses simultaneously and reduces mean‑squared error by an average of 56.5%.
"whyItMatters":"The approach demonstrates that a shared sequence‑based LLM interface can streamline cross‑family metasurface design, eliminating the need for separate surrogate architectures for each geometry class."
By Huanshu Zhang, Lei Kang, Yuyan Chen, Luxiang Wang, Zhaolong Cao, Douglas H. Werner
The paper presents a method for training single‑step neural surrogates that can handle wave‑scattering problems with tens of thousands of controllable variables. By dynamically generating training examples that highlight surrogate errors and using a replay dataset with normalization, the authors achieve a surrogate that accurately simulates two‑dimensional wave scattering for up to 41,772 variables and generalizes to over 3 million variables without retraining. The surrogate is applied to forward simulations and inverse design of freeform beam splitters and gradient‑index lenses, achieving speedups up to 26.5× compared to traditional FDTD methods.
By Charles Dove, Laura Waller
The review examines how Large Language Models (LLMs) enhance nanophotonics design by providing semantic interfaces, code generation, and tool orchestration. It traces the evolution from classical neural networks to transformer-based models and categorizes LLM applications into surrogate models that map structure to spectrum and agentic systems that generate code and orchestrate simulations for closed-loop optimization. The article also highlights potential cross-disciplinary uses of LLMs in materials science and wireless communications, and envisions future multimodal foundation models that actively collaborate in autonomous scientific discovery.
By Huanshu Zhang, Kegeng Tang, Lei Kang, Sawyer D. Campbell, Zihao Wang, Douglas H. Werner
Metasurfaces have revolutionized the development of photonic devices by enabling unprecedented precision in light manipulation. However, their design processes are often constrained by computationally...
The paper introduces a prior-guided material reparameterization for physically based inverse rendering, leveraging learned material predictions to restrict the optimization space without altering the photometric objective. By fusing multi-view predictions onto a mesh and constructing a permutohedral lattice in prior feature space, surface points with similar predictions share material variables, which are jointly optimized with geometry and illumination. Experiments on synthetic benchmarks and Stanford-ORB show improved material decomposition and relighting, with PSNR gains of 1.5–2.5 dB over strong baselines and a faster optimization schedule that maintains high relighting quality.
By Hun Hwangbo, Minjun Park, Yongsung Kim, Wooseok Song, Jungmin Ko, Junghyun Hwang, Sungroh Yoon
arXiv:2602. 08406v2 Announce Type: replace-cross Abstract: The prediction of electromagnetic spectra for MXene-based solar absorbers, where MXenes are a family of two-dimensional transition metal carbides and nitrides, is a computationally intensive task traditionally addressed using full-wave solvers.
By Shujaat Khan, Waleed Iqbal Waseer, Muhammad Shahid Jabbar
The paper introduces a self‑supervised neural network that unifies single‑frame Fresnel coherent diffraction imaging (CDI) and overlapped ptychography. By using a fixed, pre‑estimated probe and optimizing with a Poisson negative log‑likelihood objective, the method reconstructs object patches from either a single diffraction frame or multiple overlapping measurements, achieving high SSIM scores and a ten‑fold improvement in photon‑dose efficiency. Demonstrations on synthetic patterns and real datasets from APS and LCLS show robust, high‑throughput reconstructions, with a 36× speedup over iterative solvers for a 10,304‑frame workload.
By Oliver Hoidn, Steven Henke, Albert Vong, Aashwin Mishra, Apurva Mehta, Matthew Seaberg