The field of chiral plasmonics has registered considerable progress with machine-learning(ML)-mediated metamaterial prototyping,drawing from the success of ML frameworks in other applications such as pattern and image...The field of chiral plasmonics has registered considerable progress with machine-learning(ML)-mediated metamaterial prototyping,drawing from the success of ML frameworks in other applications such as pattern and image recognition.Here,we present an end-to-end functional bidirectional deep-learning(DL)model for three-dimensional chiral metamaterial design and optimization.This ML model utilizes multitask joint learning features to recognize,generalize,and explore in detail the nontrivial relationship between the metamaterials’geometry and their chiroptical response,eliminating the need for auxiliary networks or equivalent approaches to stabilize the physically relevant output.Our model efficiently realizes both forward and inverse retrieval tasks with great precision,offering a promising tool for iterative computational design tasks in complex physical systems.Finally,we explore the behavior of a sample ML-optimized structure in a practical application,assisting the sensing of biomolecular enantiomers.Other potential applications of our metastructure include photodetectors,polarization-resolved imaging,and circular dichroism(CD)spectroscopy,with our ML framework being applicable to a wider range of physical problems.展开更多
基金National Key Research and Development Program of China(2019YFB2203400)China Postdoctoral Science Foundation(2017M622992,2019M663467,2019T120820)+1 种基金National Science Foundation Emerging Frontiers&Multidisciplinary Activities(1741677)UESTC Shared Research Facilities of Electromagnetic Wave and Matter Interaction(Y0301901290100201)。
文摘The field of chiral plasmonics has registered considerable progress with machine-learning(ML)-mediated metamaterial prototyping,drawing from the success of ML frameworks in other applications such as pattern and image recognition.Here,we present an end-to-end functional bidirectional deep-learning(DL)model for three-dimensional chiral metamaterial design and optimization.This ML model utilizes multitask joint learning features to recognize,generalize,and explore in detail the nontrivial relationship between the metamaterials’geometry and their chiroptical response,eliminating the need for auxiliary networks or equivalent approaches to stabilize the physically relevant output.Our model efficiently realizes both forward and inverse retrieval tasks with great precision,offering a promising tool for iterative computational design tasks in complex physical systems.Finally,we explore the behavior of a sample ML-optimized structure in a practical application,assisting the sensing of biomolecular enantiomers.Other potential applications of our metastructure include photodetectors,polarization-resolved imaging,and circular dichroism(CD)spectroscopy,with our ML framework being applicable to a wider range of physical problems.
