Material-Centric Design of Advanced Electromagnetic Surfaces

先进的电磁表面（AESs）使令人兴奋的新型天线和无线通信组件的发展成为可能，因为它们很容易实现对电磁辐射的非凡控制。它们对于实现指令天线和自适应天线是必不可少的，而指令天线和自适应天线是5G等未来系统的基石。尽管aes衍生天线取得了进步，但它们的影响不会完全实现，直到它们同时实现更大的带宽、可重构性、效率和（关键的）整体设计的易用性。拟议的研究计划提出了一种新的以材料为中心的AESs设计方法，将其视为可以使用材料信息学技术设计的材料。目前，AESs的设计围绕着特别的、基于试错的设计方法展开，设计时间长至数周或更长。提出的方法利用计算机的进步来增强使用机器学习（ML）的AESs的设计。提出的方法将建立一个以有效材料参数为特征的AES散射体几何形状的大型数据库，允许材料信息学技术有效地对这些设计进行分类。机器学习增强设计方法将从这些数据中得到训练，并用于从一组给定的规格中快速准确地导出所需的AES散射体几何形状，将设计时间缩短到几个小时或更短。从长远来看，拟议的研究计划旨在从根本上改变AES的设计方式，并使用这种设计范式来发现新的AES功能。在短期内，拟议的研究计划旨在使用以材料为中心的设计方法设计和实施新的天线架构。三个短期目标是：1)设计在毫米波频率范围内工作的超宽带AESs；2)大幅度提高aes衍生天线的波束效率，如多波束卫星天线；3)利用所提出的方法设计可重构的AESs，以实现宽带、高效、自适应天线。该研究计划将对无线通信系统和AESs领域产生重大影响。以材料为中心的AESs将实现新的性能和功能水平，从而将下一代无线通信系统的容量提高到每秒千兆比特的水平。从长远来看，机器学习增强设计方法将通过促进其快速合成和自动发现新AES材料的平台，彻底改变AES的设计方式。通过托管一个公共的、不断增长的这些材料的数据库，我们也试图通过采用开放的方法来推动设计的变化。这种基本的新设计范式将在未来几年引领新一代AES材料，发现和设计师。

Advanced electromagnetic surfaces (AESs) have enabled the development of exciting new antennas and components for wireless communications owing to the ease with which they achieve extraordinary control of electromagnetic radiation. They are indispensable for realizing directive and adaptive antennas, which are cornerstones of future systems such as 5G. Despite advances in AES-derived antennas, their impact will not be fully realized until they simultaneously achieve greater bandwidth, reconfigurability, efficiency, and - critically - overall ease of design. The proposed research program puts forth a new material-centric design methodology for AESs that views them as materials that can be designed using material-informatic techniques. Currently, the design of AESs revolves around ad hoc, trial-and-error based design methods with lengthy design times spanning weeks or longer. The proposed approach harnesses advances in computing to augment the design of AESs using machine learning (ML). The proposed approach will build a large database of AES scatterer geometries characterized by effective material parameters, allowing techniques from material informatics to efficiently catalogue these designs. An ML-augmented design method will be trained from this data and used to quickly and accurately derive required AES scatterer geometries from a given set of specifications, cutting design times to hours or less. In the long term, the proposed research program aims to fundamentally change the way AESs are designed, and use this design paradigm to discover new AES capabilities.In the short term, the proposed research program aims to devise and implement new antenna architectures using the material-centric design approach. Three short term objectives are to: 1) devise ultra-wideband AESs operating in the millimetre-wave frequency range; 2) greatly improve the beam efficiency of AES-derived antennas, such as multi-beam satellite antennas; and 3) devise reconfigurable AESs using the proposed approach in order to achieve wideband, high efficiency, adaptive antennas.The proposed research program will have major impact on wireless communication systems and the field of AESs. AESs derived from the material-centric approach will achieve new levels of performance and functionality, thereby increasing the capacity of next-generation wireless communication systems to multi-gigabit-per-second levels. In the long run, the ML-augmented design approach will revolutionize the way AESs are designed by facilitating both their rapid synthesis and a platform for automated discovery of new AES materials. By hosting a public, ever-growing database of these materials, we also seek to drive change in their design through embracing an open approach. This fundamental new design paradigm will usher in a new generation of AES materials, discoveries, and designers for years to come.