用扩散模型逆向设计可制造的超材料，精准控制光谱响应。
Inverse Design of Metamaterials with Manufacturing-Guiding Spectrum-to-Structure Conditional Diffusion Model
- 基于条件扩散模型，从目标光谱反推结构与尺寸参数。
- 生成多样结构且光谱预测精度高，支持热隐身等实际应用。
- 结果提供制造先验知识，适合需要可加工设计的研究者。
超材料是人工构造的电磁波调控结构，具备自然材料所不具备的光学特性。近年来，机器学习在超材料逆向设计中受到关注。然而，超材料结构与光学行为之间存在高度非线性关系，加之制造难度大，使得机器学习在设计复杂超材料时面临挑战。本文提出一种通用框架，利用条件扩散模型实现定制化的光谱到结构与尺寸参数的映射，解决一对多的超材料逆向设计问题。该方法表现出优异的光谱预测准确率，生成的结构多样性优于其他典型生成模型，并通过分析生成结果提供宝贵的制造先验知识，从而促进超材料设计的实际制备。我们成功设计并制备了一种自由形貌的超材料，其具有定制化的选择性发射光谱，适用于热隐身应用。
原文摘要 · Abstract (English)
Metamaterials are artificially engineered structures that manipulate electromagnetic waves, having optical properties absent in natural materials. Recently, machine learning for the inverse design of metamaterials has drawn attention. However, the highly nonlinear relationship between the metamaterial structures and optical behaviour, coupled with fabrication difficulties, poses challenges for using machine learning to design and manufacture complex metamaterials. Herein, we propose a general framework that implements customised spectrum-to-shape and size parameters to address one-to-many metamaterial inverse design problems using conditional diffusion models. Our method exhibits superior spectral prediction accuracy, generates a diverse range of patterns compared to other typical generative models, and offers valuable prior knowledge for manufacturing through the subsequent analysis of the diverse generated results, thereby facilitating the experimental fabrication of metamaterial designs. We demonstrate the efficacy of the proposed method by successfully designing and fabricating a free-form metamaterial with a tailored selective emission spectrum for thermal camouflage applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。