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DOE OSTI2023

Synthetically non-Hermitian nonlinear wave-like behavior in a topological mechanical metamaterial

Topological mechanical metamaterials have enabled new ways to control stress and deformation propagation. Exemplified by Maxwell lattices, they have been studied extensively using a linearized formalism. Herein, we study a two-dimensional topological Maxwell lattice by exploring its large deformation quasi-static response using geometric numerical simulations and experiments. We observe spatial nonlinear wave-like phenomena such as harmonic generation, localized domain switching, amplification-enhanced frequency conversion, and solitary waves. We further map our linearized, homogenized system to a non-Hermitian, nonreciprocal, one-dimensional wave equation, revealing an equivalence between the deformation fields of two-dimensional topological Maxwell lattices and nonlinear dynamical phenomena in one-dimensional active systems. Our study opens a regime for topological mechanical metamaterials and expands their application potential in areas including adaptive and smart materials and mechanical logic, wherein concepts from nonlinear dynamics may be used to create intricate, tailored spatial deformation and stress fields greatly transcending conventional elasticity.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Rippled metamaterials with scale-dependent tailorable elasticity

Thermally induced ripples are intrinsic features of nanometer-thick films, atomically thin materials, and cell membranes, significantly affecting their elastic properties. Despite decades of theoretical studies on the mechanics of suspended thermalized sheets, controversy still exists over the impact of these ripples, with conflicting predictions about whether elasticity is scale-dependent or scale-independent. Experimental progress has been hindered so far by the inability to have a platform capable of fully isolating and characterizing the effects of ripples. This knowledge gap limits the fundamental understanding of thin materials and their practical applications. Here, we show that thermal-like static ripples shape thin films into a class of metamaterials with scale-dependent, customizable elasticity. Utilizing a scalable semiconductor manufacturing process, we engineered nanometer-thick films with precisely controlled frozen random ripples, resembling snapshots of thermally fluctuating membranes. Resonant frequency measurements of rippled cantilevers reveal that random ripples effectively renormalize and enhance the average bending rigidity and sample-to-sample variations in a scale-dependent manner, consistent with recent theoretical estimations. The predictive power of the theoretical model, combined with the scalability of the fabrication process, was further exploited to create kirigami architectures with tailored bending rigidity and mechanical metamaterials with delayed buckling instability.

Applied Physical Sciences↗

DOE OSTI2021

Mechanical response of shape-recovering metamaterial structures fabricated by additive manufacturing

Abstract Three different metamaterial structures were fabricated using stereolithography 3D printing and a shape recovering material. Mechanical properties and recovery efficiency were assessed after compression testing. All three structures exhibited similar initial specific compressive moduli, while the highest specific toughness was observed for the stretch-dominated structure. The three metamaterial structures were re-tested after shape recovery. Significant strengthening was observed for all structures, with the bend-stretch-dominated structure strengthening to the highest degree. This strengthening phenomenon was characterized as strain hardening. It was found that the strengthening is highly geometry dependent. The geometry with stretch-dominated behavior exhibited the highest mechanical properties after a second test was performed. Improvements in specific toughness of up to 67% were observed after the second compressive test.

36 MATERIALS SCIENCE↗

DOE OSTI2022

ZnO-Au x Cu 1-x Alloy and ZnO-Au x Al 1-x Alloy Vertically Aligned Nanocomposites for Low-Loss Plasmonic Metamaterials

Hyperbolic metamaterials are a class of materials exhibiting anisotropic dielectric function owing to the morphology of the nanostructures. In these structures, one direction behaves as a metal, and the orthogonal direction behaves as a dielectric material. Applications include subdiffraction imaging and hyperlenses. However, key limiting factors include energy losses of noble metals and challenging fabrication methods. In this work, self-assembled plasmonic metamaterials consisting of anisotropic nanoalloy pillars embedded into the ZnO matrix are developed using a seed-layer approach. Alloys of Au x Al 1-x or Au x Cu 1-x are explored due to their lower losses and higher stability. Optical and microstructural properties were explored. The ZnO-Au x Cu 1-x system demonstrated excellent epitaxial quality and optical properties compared with the ZnO-Au x Al 1-x system. Both nanocomposite systems demonstrate plasmonic resonance, hyperbolic dispersion, low losses, and epsilon-near-zero permittivity, making them promising candidates towards direct photonic integration.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Self‐Assembled Metal–Dielectric Hybrid Metamaterials in Vertically Aligned Nanocomposite Form with Tailorable Optical Properties and Coupled Multifunctionalities

Metal–dielectric hybrid metamaterials have attracted increasing research interest in recent years because of their novel optical properties and promising applications in the fields of electronic and photonic devices. Dielectric permittivity is a key parameter that strongly influences the optical properties of materials. By self‐assembling the metallic and dielectric components in a pillar‐in‐matrix structure, a strong anisotropic structure forms and results in opposite signs of permittivity components (i.e., ε || > 0 and ε ⊥ < 0, or vice versa) and exotic optical responses including hyperbolic dispersion in the visible to near‐infrared region. Herein, the main approaches of tuning the permittivity in self‐assembled metal–dielectric vertically aligned nanocomposite (VAN) thin films are reviewed, including tuning the metal pillar density and geometry, film strain state and background pressure during growth, and seeking other metal‐free and complex structure designs. Future research directions are also proposed, including unique approaches to improve their thermal stability, integrate on flexible substrates toward wearable device fabrication, and explore real‐time tunable metamaterials.

Zhang, Di↗

DOE OSTI2023

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.

Sarkar, Sulagna↗

DOE OSTI2025

Integrating magnetic Co-nanopillars in a NbN-based VAN thin film as a multifunctional hybrid metamaterial

NbN thin films are considered as a promising alternative candidate to conventional Al superconducting electrodes for Josephson junction devices, superconducting qubits and quantum logic circuits. Herein, by co-growing ferromagnetic Co with superconducting NbN, self-assembled NbN–Co vertically aligned nanocomposite (VAN) thin films have been successfully deposited on a MgO substrates, with NbN in its cubic superconducting phase. The obtained VAN metamaterials exhibit epitaxial growth of NbN with uniformly dispersed Co nanoplates. Further physical property characterization reveals that the NbN–Co VAN presents ferromagnet properties with strong out-of-plane magnetic anisotropy and optical anisotropy. This hybrid metamaterial system could find future applications in superconducting spintronic devices and quantum computing devices.

Zhang, Yizhi [Purdue Univ., West Lafayette, IN (Un↗

DOE OSTI2025

Morphology and property tuning in ZnO–Ni hybrid metamaterials in vertically aligned nanocomposite (VAN) form

ZnO thin films have attracted significant interest in the past decades owing to their unique wide band gap properties, piezoelectric properties, non-linearity and plasmonic properties. Recent efforts have been made in coupling ZnO with secondary phases to enhance its functionalities, such as Au–ZnO nanocomposite thin films with tunable optical and plasmonic properties. In this work, magnetic nanostructures of Ni are incorporated in ZnO thin films in a vertically aligned nanocomposite (VAN) form to couple magnetic and plasmonic response in a complex hybrid metamaterial system. Nickel (Ni) is of interest due to its ferromagnetic and plasmonic properties along with gold (Au) which is also plasmonic. Therefore, two approaches, namely, tuning of the deposition pressure and use of a ZnO–Au seeding layer have been attempted to achieve unique Ni nanostructures in addition to tuning of the microstructure. Together, both approaches demonstrate a range of microstructures such as core–shell, nanodisk, nanocup, and nanocube-like morphologies not previously attempted. Additionally, there is effective tuning of properties. Specifically, the seeding layer thickness causes hyperbolic behavior as well as redshift in the surface plasmon resonance (SPR) wavelength. The addition of the ZnO–Au seeding layer directly influences the optical properties. Plus, regardless of the different approaches, the films demonstrate magnetic anisotropy based on the composition and microstructure of the film which impacted the saturation magnetization and coercivity. This study demonstrates the potential of ZnO-based complex hybrid metamaterials with coupled electro-magneto-optical properties for integrated photonic devices.

Bhatt, Nirali A. [Purdue Univ., West Lafayette, IN↗

DOE OSTI2024

Elastic wave suppression through additively manufactured petal lattice metamaterials

Lattice-core sandwich structure metamaterials are lightweight alternatives to monolithic materials that can present better mechanical, thermal, and energy dampening performance. Manufacturing lattice metamaterials to follow curved surfaces can pose a challenge, as the lattices rely on their geometric orientation to the substrate for their mechanical properties. This work rationally designed a lattice structure where the surface is broken up into “petals” connected to the underlying lattice, which localizes the petals’ impact response. This design opens a pathway for implementation of lattice-core sandwich structures onto complex surface geometries. These petal structures were evaluated for their energy absorption efficiency experimentally by utilizing pressure waves generated with nanosecond lasers and computationally via finite element modeling. The lattice structures exhibited a two-orders-of-magnitude decrease in transmitted pressure compared to their constituent steel at equivalent mass. Furthermore, localizing energy absorption into petal structures provided a 44% reduction in peak load compared to a continuous “single-petal” design.

Sarracino, Alex↗

DOE OSTI2020

Demonstration of a third-order hierarchy of topological states in a three-dimensional acoustic metamaterial

Classical wave systems have constituted an excellent platform for emulating complex quantum phenomena, such as demonstrating topological phenomena in photonics and acoustics. Recently, a new class of topological states localized in more than one dimension of a D -dimensional system, referred to as higher-order topological (HOT) states, has been reported, offering an even more versatile platform to confine and control classical radiation and mechanical motion. Here, we design and experimentally study a 3D topological acoustic metamaterial supporting third-order (0D) topological corner states along with second-order (1D) edge states and first-order (2D) surface states within the same topological bandgap, thus establishing a full hierarchy of nontrivial bulk polarization–induced states in three dimensions. The assembled 3D topological metamaterial represents the acoustic analog of a pyrochlore lattice made of interconnected molecules, and is shown to exhibit topological bulk polarization, leading to the emergence of boundary states.

Science & Technology - Other Topics↗

DOE OSTI2020

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the finite element method (FEM). The development of physics-informed deep learning techniques for inverse scattering can enable the design of novel functional nanostructures and significantly broaden the design space of metamaterials by naturally accounting for radiation and finite-size effects beyond the limitations of traditional effective medium theories.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Putting a spin on metamaterials: Mechanical incompatibility as magnetic frustration

Mechanical metamaterials present a promising platform for seemingly impossible mechanics. They often require incompatibility of their elementary building blocks, yet a comprehensive understanding of its role remains elusive. Relying on an analogy to ferromagnetic and antiferromagnetic binary spin interactions, we present a general approach to identify and analyze topological mechanical defects for arbitrary building blocks. We underline differences between two- and three-dimensional metamaterials, and show how topological defects can steer stresses and strains in a controlled and non-trivial manner and can inspire the design of materials with hitherto unknown complex mechanical response.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials

Optical metamaterial device design has enjoyed a long track of success over the past 50 years leading to the manipulation of light over a wide range of wavelengths spanning the ultraviolet to the far infrared. The manipulation of light over such wavelengths has already led to many technological advancements such as the design of selective radiative absorbers for solar energy, daytime passive cooling using deep space, and optical invisibility cloaks for defense applications. Further disruptive advancements in energy, defense, computing, and biomedical fields could be enabled or enhanced by future optical metamaterial devices. These technologies could lead to increased energy efficiency and hence reduced national primary energy consumption, cheap long duration energy storage, and next generation solid-state heat engines. But historically the methods to invent and develop all of these devices have been time- consuming and based mostly on intuition and iteration. Finding an optimal design can take years.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials

36 MATERIALS SCIENCE↗

DOE OSTI2025

ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers

Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.

large convolution kernel↗

DOE OSTI2025

Tunable Epsilon Near Zero Metamaterial with Rotating Obround-Shaped Meta-Atoms

A new design of a microwave-range ENZ metamaterial consisting of rods with an obround cross-section is proposed. The plasma frequency of the metamaterial can be tuned by rotating the constituent meta-atoms. Tunability of the plasma frequency by 26% is demonstrated both experimentally and numerically. The observed tuning range is dramatically higher than in the one observed in natural materials at optical range.

Balafendiev, Rustam [ITMO U., St. Petersburg; Icel↗

DOE OSTI2021

Imaging devices including dielectric metamaterial absorbers and related methods

Imaging devices including dielectric metamaterial absorbers and related methods are disclosed. According to an aspect, an imaging device includes a support. The imaging device also includes multiple dielectric metamaterial absorbers attached to the support. Each absorber includes one or more dielectric resonators configured to generate and emit thermal heat upon receipt of electromagnetic energy.

Padilla, Willie John↗

DOE OSTI2024

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

Kim, Seongmin↗