Data from: Benchmarking deep learning architectures for artificial electromagnetic material problems

Artificial electromagnetic materials (AEMs), including metamaterials, derive their electromagnetic properties from geometry rather than chemistry. With the appropriate geometric design, AEMs have achieved exotic properties not realizable with conventional materials (e.g., cloaking or negative refractive index). However, understanding the relationship between the AEM structure and its properties is often poorly understood. While computational electromagnetic simulation (CEMS) may help design new AEMs, its use is limited due to its long computational time. Recently, it has been shown that deep learning can be an alternative solution to infer the relationship between an AEM geometry and its properties using a (relatively) small pool of CEMS data. However, the limited publicly released datasets and models and no widely-used benchmark for comparison have made using deep learning approaches even more difficult. Furthermore, configuring CEMS for a specific problem requires substantial expertise and time, making reproducibility challenging. Here, we develop a collection of three classes of AEM problems: metamaterials, nanophotonics, and color filter designs. We also publicly release software, allowing other researchers to conduct additional simulations for each system easily. Finally, we conduct experiments on our benchmark datasets with three recent neural network architectures: the multilayer perceptron (MLP), MLP-mixer, and transformer. We identify the methods and models that generalize best over the three problems to establish the best practice and baseline results upon which future research can build.人工电磁材料（Artificial Electromagnetic Materials，AEMs）包括超材料，其电磁特性由几何结构决定，而非化学组分。通过合理的几何设计，人工电磁材料可实现传统材料无法达成的奇异电磁特性，例如电磁隐身与负折射率。然而，学界对人工电磁材料的结构与其特性之间的关联机制仍缺乏清晰认知。尽管计算电磁仿真（Computational Electromagnetic Simulation，CEMS）可助力新型人工电磁材料的设计，但其高昂的计算时长限制了其应用范围。近年来，研究表明深度学习可作为替代方案，利用规模相对有限的计算电磁仿真数据集，推断人工电磁材料的几何结构与其特性之间的关联。但目前公开可用的数据集与模型数量有限，且缺乏通用的对比基准，这进一步加大了深度学习方法的应用难度。此外，针对特定任务配置计算电磁仿真系统需要深厚的专业知识与大量时间成本，导致研究可复现性面临挑战。本研究构建了涵盖三类人工电磁材料任务的数据集：超材料、纳米光子学以及彩色滤光片设计任务。同时，我们公开了配套软件，便于其他研究人员针对各类系统开展额外的仿真实验。最后，我们基于构建的基准数据集，针对三类新近提出的神经网络架构开展了对比实验：多层感知机（Multilayer Perceptron，MLP）、MLP-mixer以及Transformer。我们遴选出在三类任务上泛化性能最优的方法与模型，确立了本领域的最佳实践与基准结果，可为后续研究提供参考基础。