Magnetic Resonances in Nonlinear Dielectric Nanostructures: New Light-Matter Interactions and Machine Learning Enhanced Design

非线性介电纳米结构中的磁共振：新的光-物质相互作用和机器学习增强设计

• 批准号: 2240562
• 负责人: Natalia Litchinitser
• 金额: $ 45万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240562&HistoricalAwards=false
• 关键词: Magnetic; Resonances; Nonlinear; Dielectric; Nanostructures

Interactions between light and matter play an important role in many fields of science, giving rise to important applications in sensing, spectroscopy, solar cells, computing, quantum information processing, communications, light-emitting diodes, and lasers. While light is an electromagnetic wave consisting of both electric and magnetic field components, most natural optical materials mainly interact with the electric component of light and leave the magnetic component of light largely unexploited. However, optical metamaterials—engineered nanostructures— fundamentally change the light-matter interaction by making light "ambidextrous" in the optical range, with its magnetic and electric components playing equally important roles In particular, judiciously designed dielectric particles provide strong light-induced magnetic properties as a reaction to the magnetic component of external electromagnetic waves. The proposed project aims at discovering new light-matter interactions originating from the effect of magnetic field enhancement in low-loss nonmagnetic dielectrics as well as hybrid metamaterials made of strongly nonlinear glasses and magnetic materials designed using physics-based machine learning approaches. The proposed research will contribute to the fundamental science of nonlinear light-matter interactions and will likely enable new approaches for light generation and modulation, magnetometry, and sensing.While nonlinear optical interactions, enabled by the electric field enhancement, have been studied by many research groups, magnetic field enhancement-induced light-matter interactions have not been explored in detail. The proposed project aims at discovering new light-matter interactions originating from the effect of magnetic field enhancement, contributing through a magnetic portion of the Lorentz force that contains intrinsic surface and bulk components in engineered optical materials designed using physics-based machine learning based optimization of the resonant and magneto-optical nonlinear interactions in subwavelength single-layer or cascaded metasurfaces consisting of hybrid meta-atoms made of strongly nonlinear chalcogenide glasses and magnetic materials. The proposed research will focus on the following thrusts: Thrust 1: Investigate theoretically and numerically the contribution of the intrinsic nonlinear optical processes in nonlinear metasurfaces due to the magnetic portion of the Lorentz force; Thrust 2: Using the synergy between the physical model and machine learning approach, enhance nonlinear light-matter interactions due to the strongly localized magnetic fields via Mie, quasi-bound states in the continuum, and guided-mode mechanisms; enhance extrinsic, magneto-optical interactions enabled by hybrid magneto-photonic materials based metasurfaces. Thrust 3: Investigate theoretically and experimentally the possibility of enhancement of the nonlinear frequency conversion in stacked metasurfaces and broadband tapered multilayer structures. Exceptionally high magnetic field localization inside the meta-atoms will be achieved using a unique, physics-based machine learning approach developed to solve the inverse design problem of resonant meta-atoms under the guidance of multipole expansion theory to optimize the shape of the meta-atoms in order to maximize a particular multipolar resonance and the overlap of the modes at both fundamental frequency and a harmonic wavelength.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

光与物质之间的相互作用在许多科学领域都起着重要的作用，从而在感应，光谱，太阳能电池，计算，量子信息处理，通信，发光语音和激光器中引起了重要的应用。虽然光是由电场和磁场组件组成的电子波，但大多数天然光学材料主要与光的电动组件相互作用，并留下光的磁成分，在很大程度上出乎意料。然而，光学超材料（设计的纳米结构）通过在光学范围内使光的“ Ambidextrous”在极端的角度上改变了磁性相互作用，其磁性和电动成分在尤其是明智地设计的饮食颗粒中扮演着同样重要的作用，尤其是具有明智设计的饮食颗粒，可提供强大的光诱导的磁性特性，因为它对外部电子电波的磁性组件的反应。拟议的项目旨在发现新的光结合相互作用，源于低损失非磁性饮食学中磁场增强的影响以及由强烈非线性玻璃和使用基于物理机器的机器学习方法设计的磁性材料制成的混合材料。拟议的研究将有助于非线性光 - 互动的基本科学，并可能为发光和调制，磁力测定和敏感性提供新的方法。虽然由电场增强的非线性光学相互作用启用，但已由许多研究小组研究，但磁场增强了磁场诱导的光效率相互作用尚未得到详细介绍。 The proposed project aims at discovering new light-matter interactions originating from the effect of magnetic field enhancement, contributing through a magnetic portion of the Lorentz force that contains intrinsic surface and bulk components in engineered optical materials designed using physics-based machine learning based optimization of the resonant and magneto-optical nonlinear interactions in subwavelength single-layer or cascaded meta-atoms making of strongly nonlinear葡萄干剂玻璃和磁性材料。拟议的研究将重点放在以下推力上：推力1：由于洛伦兹力的磁性部分而在非线性跨曲面中固有非线性光学过程的贡献进行研究；推力2：使用物理模型和机器学习方法之间的协同作用，可以通过MIE，持续的准结合状态和指导模式机制来增强非线性光 - 物质相互作用。通过基于混合磁磁性材料的元整日来增强外在的，磁光的相互作用。推力3：在理论和实验上研究了在堆叠的跨曲面和宽带锥形多层结构中提高非线性频率转换的可能性。在元原子内部将使用一种独特的基于物理的机器学习方法来实现元原子内部的异常高磁场的定位，以在多物扩展理论的指导下求解共振的元原子的逆设计问题，以优化元原子的形状，以优化特定的多极和统计的统计信息，并在基础上的统计数字中最大程度地提高基础频率的统计信息。使用基金会的知识分子优点和更广泛的审查标准，通过评估被认为值得支持。