Deep Learning-Assisted Inverse Design of Transparent Absorbers Based on Ionic Liquids Using Mixture Density Networks

Abstract Machine learning has emerged as a powerful tool for the inverse design of various device structures including metamaterials and multilayer coatings. This study presents an inverse design approach for transparent wave absorbers based on multiple ionic liquids, employing a mixture density network (MDN) architecture. The model focuses on the perfect absorption bandwidth as the design objective, treating both the ionic liquid type and the layer-specific structural parameters as design variables. It enables rapid prediction of design variables that meet specified conditions, offering multiple viable structural configurations, even as design goals change. Compared with other inverse design methods, this approach is highly practical and provides a broad range of solutions, facilitating the identification of the optimal configuration. Using this model, we designed a transparent broadband wave absorber with an absorption bandwidth of 4.18 to 34.9 GHz, an average transmittance of 76.5%, and a thickness of only 8.725 mm, achieving a high bandwidth while maintaining high transparency. This absorber is well-suited for applications in fighter cockpit shields and microwave anechoic chambers.