# A Generative Model for Inverse Design of Metamaterials

arXiv: https://arxiv.org/abs/1805.10181 · HTML: https://mlsanity.com/paper/1805.10181/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 27 of 27)

- [Physics-informed neural networks with hard constraints for inverse design](https://mlsanity.com/paper/2102.04626/) (2021)
- [IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures](https://mlsanity.com/paper/2103.02588/) (2021)
- [Inverse deep learning methods and benchmarks for artificial electromagnetic material design](https://mlsanity.com/paper/2112.10254/) (2021)
- [Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design](https://mlsanity.com/paper/2304.13038/) (2023)
- [Text2Robot: Evolutionary Robot Design from Text Descriptions](https://mlsanity.com/paper/2406.19963/) (2024)
- [A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials](https://mlsanity.com/paper/2303.16412/) (2023)
- [HiLAB: A Hybrid Inverse-Design Framework](https://mlsanity.com/paper/2505.17491/) (2025)
- [How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning](https://mlsanity.com/paper/2111.05949/) (2021)
- [Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes](https://mlsanity.com/paper/2202.13309/) (2022)
- [Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces](https://mlsanity.com/paper/2102.01761/) (2021)
- [Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems](https://mlsanity.com/paper/2204.13376/) (2022)
- [Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning](https://mlsanity.com/paper/2401.00003/) (2023)
- [Inverse Design of Metamaterials with Manufacturing-Guiding Spectrum-to-Structure Conditional Diffusion Model](https://mlsanity.com/paper/2506.07083/) (2025)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing](https://mlsanity.com/paper/2406.19397/) (2024)
- [Pentagonal Photonic Crystal Mirrors: Scalable Lightsails with Enhanced Acceleration via Neural Topology Optimization](https://mlsanity.com/paper/2407.07896/) (2024)
- [Anchor-Controlled Generative Adversarial Network for High-Fidelity Electromagnetic and Structurally Diverse Metasurface Design](https://mlsanity.com/paper/2408.16231/) (2024)
- [Univariate Conditional Variational Autoencoder for Morphogenic Patterns Design in Frontal Polymerization-Based Manufacturing](https://mlsanity.com/paper/2410.17518/) (2024)
- [PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing](https://mlsanity.com/paper/2412.19284/) (2024)
- [Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces](https://mlsanity.com/paper/2511.03735/) (2025)
- [Generating extreme quantum scattering in graphene with machine learning](https://mlsanity.com/paper/2212.06929/) (2022)
- [Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging](https://mlsanity.com/paper/2305.03177/) (2023)
- [Solar Irradiance Anticipative Transformer](https://mlsanity.com/paper/2305.18487/) (2023)
- [A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design](https://mlsanity.com/paper/2401.02961/) (2023)
- [Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space](https://mlsanity.com/paper/2408.05917/) (2024)
- [Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints](https://mlsanity.com/paper/2504.17142/) (2025)
- [Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization](https://mlsanity.com/paper/2506.20056/) (2025)

## Abstract (arXiv)

The advent of two-dimensional metamaterials in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The effective parameters of these architected materials render unprecedented control over the optical properties of light, thereby eliciting previously unattainable applications in flat lenses, holographic imaging, and emission control among others. The design of such structures, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively solve Maxwell's equations until a locally optimized solution can be attained. In this work, we identify a solution to circumvent this intuition-guided design by means of a deep learning architecture. When fed an input set of optical spectra, the constructed generative network assimilates a candidate pattern from a user-defined dataset of geometric structures in order to match the input spectra. The generated metamaterial patterns demonstrate high fidelity, yielding equivalent optical spectra at an average accuracy of about 0.9. This approach reveals an opportunity to expedite the discovery and design of metasurfaces for tailored optical responses in a systematic, inverse-design manner.