SEARCH · Search NASA

Results for “Mechanical Metamaterials”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

All sources NASA NTRS DOE OSTI

NASA NTRS2024

Ultra-Light, Strong, and Self-Reprogrammable Mechanical Metamaterials

Versatile programmable materials have long been envisioned that can reconfigure themselves to adapt to changing use cases in adaptive infrastructure, space exploration, disaster response, and more. We introduce a robotic structural system as an implementation of programmable matter, with mechanical performance and scale on par with conventional high-performance materials and truss systems. Fiber reinforced composite truss-like building-blocks form strong, stiff, and lightweight lattice structures as mechanical metamaterials. Two types of mobile robots operate over the exterior surface and through the interior of the system, performing transport, placement, and reversible fastening using the intrinsic lattice periodicity for indexing and metrology. Leveraging programmable matter algorithms to achieve scalability in size and complexity, this system design enables robust collective automated assembly and reconfiguration of large structures with simple robots. We describe the system design and experimental results from a 256-unit cell assembly demonstration and lattice mechanical testing, as well as demonstration of disassembly and reconfiguration. The assembled structural lattice material exhibits ultra-light mass density (0.0103 g/cc) with high strength and stiffness for its weight (0.01117 MPa and 1.1129 MPa, respectively), a material performance realm appropriate for applications like space structures. With simple robots and structure, high mass-specific structural performance, and competitive throughput, this system demonstrates potential for self-reconfiguring autonomous metamaterials for diverse applications.

Robotics↗

NASA NTRS2021

Chapter 5: Mechanical metamaterials and topological soft matter: allostery and auxetics--distributed energetics and mutation upon deployment

At the suggestion of NASA’s Physical Science Research Program in the Space Life and Physical Science Research and Application Division, Paul Chaikin, Noel Clark, and Sidney Nagel organized a focus session and workshop for the 2020 American Physical Society (APS) March meeting under the auspices of the Division of Soft Matter. Three overarching themes emerged from the workshop and are presented with additional details: • Machines made out of machines • Scalable self-sustaining ecosystems • Active materials and metamaterials This report lays out only some of the potential directions for soft matter dynamics over the next two decades. It also lays out the role that gravity plays in the organization of the basic building blocks of matter. Not only will research on soft matter have tremendous application towards understanding its behavior in our terrestrial environment, but also potentially in other NASA programs such as planetary science, exploration, robotics, etc. Attached is a White Paper for the Decadal Survey that consists of an extended Title along with the previous Introduction and Chapter 2.5 from NASA/CP-20205010493.

Soft matter↗

DOE OSTI2025

High compressive energy absorption and shape recovery behavior of additively manufactured textile-inspired cylindrical braided metamaterials

Mechanical metamaterials (MMs) are engineered structures with unique mechanical properties that arise from their unique spatial arrangement or lattice-like structure. The most commonly designed MMs such as honeycomb and re-entrant auxetics are prone to failure at the sharp corners and weak joints due to the increased stress concentration under deformation. To mitigate this challenge, braided MM structures involving intertwining threads of nylon—forming curved unit cells—have been studied. These textile-inspired cylindrical braided metamaterials (CBMMs) with contrasting unit cells, namely diamond and regular CBMMs, were fabricated by 3D printing. The layer-by-layer deposited structure built by fused filament fabrication delivered an assembly of overlapped threads that are fused at the contact point. To understand deformation behavior of these MMs, finite element models were developed for various load scenarios including quasi-static compression, cyclic and creep loads at room temperature. Stress distribution, deformation mechanisms, and failure modes were analyzed and validated by experiments to analyze the geometries and associated performance. The diamond CBMMs showed stress softening at 30 % compressive strain, withstanding a load of ∼440 N, whereas the regular CBMMs at 50 % strain experienced ∼250 N. The diamond CBMMs delivered higher creep resistance under sustained load and better energy absorption under cyclic loading than the regular CBMMs. The latter, however, exhibited 94 % shape recovery in contrast to 88 % recovery in former prototype during their first cyclic load. In conclusion, this study helps design mechanical lightweight devices that endure significant sustained load and exhibit enhanced energy absorption and shape recovery characteristics in cyclic loading.

Creep↗

DOE OSTI2024

Emergent disorder and mechanical memory in periodic metamaterials

Ordered mechanical systems typically have one or only a few stable rest configurations, and hence are not considered useful for encoding memory. Multistable and history-dependent responses usually emerge from quenched disorder, for example in amorphous solids or crumpled sheets. In contrast, due to geometric frustration, periodic magnetic systems can create their own disorder and espouse an extensive manifold of quasi-degenerate configurations. Inspired by the topological structure of frustrated artificial spin ices, we introduce an approach to design ordered, periodic mechanical metamaterials that exhibit an extensive set of spatially disordered states. While our design exploits the correspondence between frustration in magnetism and incompatibility in meta-mechanics, our mechanical systems encompass continuous degrees of freedom, and thus generalize their magnetic counterparts. We show how such systems exhibit non-Abelian and history-dependent responses, as their state can depend on the order in which external manipulations were applied. We demonstrate how this richness of the dynamics enables to recognize, from a static measurement of the final state, the sequence of operations that an extended system underwent. Thus, multistability and potential to perform computation emerge from geometric frustration in ordered mechanical lattices that create their own disorder.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Multi-objective automatic discovery of optimized metamaterials for varying velocity impact protection

Mechanical metamaterials have demonstrated exceptional impact performance while remaining lightweight. Impact resistance has traditionally been investigated using quasi-static simulations, often with the assumption that performance will translate to high-velocity impact scenarios. However, critical crash protection parameters—such as peak stress and absorbed energy—are highly sensitive to impact velocity, leading to inconsistent performance under dynamic loading. To address this, we introduce a strain-rate-aware, active deep learning framework that enables multi-objective optimization of impact protection metrics across a wide range of impact velocities. Our framework captures the strain-rate sensitivity of architected lattices by learning to control spatial gradation in cellular metamaterials, resulting in over 200 % enhancement in impact protection relative to state-of-the-art designs such as Voronoi and re-entrant lattices. We demonstrate its practical utility by designing next-generation lattice structures for automotive bumper systems that satisfy multiple, velocity-specific safety criteria—capabilities beyond those of conventional designs. More than just a predictive tool, this framework marks the first step towards enabling adaptable impact-resistant structures across dynamic regimes.

Deep learning↗

DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗

DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Micro-architected material design for mechanical response

Rapid advances in additive manufacturing (AM) have enabled the creation of micro-architected materials—also known as mechanical metamaterials—with unprecedented control over fine-scale geometries and arrangements of multiple material constituents. These “materials” can achieve unique and extraordinary effective mechanical properties through their complex architectures rather than composition alone. A key challenge is to design for these bespoke effective mechanical responses within the constraints of available AM techniques (i.e., given a set of desired effective properties), identify a (often nonunique) micro-architecture and selection of material constituents that achieves them. Two main strategies have emerged. Gradient-based methods use sensitivity analysis to iteratively refine candidate designs, while data-driven methods learn micro-architecture-constituent relationships from existing examples to propose new designs. This article reviews these design approaches for micro-architected materials with tailored mechanical responses that can be fabricated by AM as well as their applications.

Spadaccini, Christopher M [Lawrence Livermore Nati↗

DOE OSTI2025

Realizing mechanical frustration at the nanoscale using DNA origami

Structural designs inspired by physical and biological systems have been previously utilized to develop mechanical metamaterials with enhanced properties based on clever geometric arrangement of constituent building blocks. Here, we use the DNA origami method to realize a nanoscale metastructure exhibiting mechanical frustration, a counterpart of the well-known phenomenon of magnetic frustration. By selectively actuating reconfigurable struts, it adopts either frustrated or non-frustrated states, each characterized by distinct free energy profiles. While the non-frustrated state distributes the strain homogeneously, the frustrated mode concentrates it at a specific location. Molecular dynamics simulations reconcile the contrasting behaviors and provide insights into underlying mechanics. We explore the design space further by tailoring responses through structural modifications. Our work combines programmable DNA self-assembly with mechanical design principles to overcome engineering limitations encountered at the macroscale to design dynamic, deformable nanostructures with potential applications in elastic energy storage, nanomechanical computation, and allosteric mechanisms in DNA-based nanomachinery.

DNA nanostructures↗

DOE OSTI2025

Multi-Scale Design And Advanced Manufacturing Of Seismic Metamaterials (CRADA Final Report)

As part of the Cyclotron Road program, METAseismic sought to investigate a multi-scale approach, both in terms of manufacturing and design, for seismic metamaterials with applications in the energy sector. Specifically, this project explored the feasibility of using nanofabrication to enable the incorporation of 3D nanostructured components into a mechanical metamaterial. These components were explored to increase the seismic resilience of our infrastructure to earthquakes, with the final aim of providing customized levels of protection for its components. This was envisioned as the base of a multi-scale design approach that consisted of using a multiplicity of metamaterial architectures ranging from the nano- to the meso-scale to address the stringent seismic design objectives of electric-power equipment.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Rapid Lightweight Firmware Architecture of the Mobile Metamaterial Internal Co-Integrator Robot

The Mobile Metamaterial Internal Co-Integrator (MMIC-I) is a structure assembly and servicing robot for in-space servicing, assembly, and manufacturing of primary structures and infrastructure. MMIC-I is a battery-powered crawling robot that can travel through periodic structures such as trusses and open framework mechanical metamaterials. It does this through sequences of component extension, contraction, and gripping. This paper provides a detailed discussion of MMIC-I’s lightweight and rapidly developed firmware architecture, to enable demonstration of robot locomotion, secondary operations, and communications with a central command source. The rationale for the lightweight rapid development approach is to allow for assessment of long term system requirements in parallel with the mechatronics development, including optimization of system and subsystem power densities, to inform a future choice of flight ready software frameworks. MMIC-I system computing and I/O requirements are much lower than what is provided by proven baseline computing hardware for existing flight ready software frameworks such as the core Flight System, F prime, and the Robot Operating System. Development of earth gravity ground demonstration of the robotic systems is greatly benefited by limited power and mass factors for computing hardware. Here, we implement inter-process communication, commanding, and telemetry with the Espressif ESP32 module running the Arduino OS.

Damiana Catanoso↗

DOE OSTI2025

Strongly nonlinear wave propagation in elasto-plastic metamaterials: Low-order dynamic modeling

Nonlinear elastic metamaterials are known to support a variety of dynamic phenomena that enhance our capacity to manipulate elastic waves. Since these properties stem from complex, subwavelength geometry, full-scale dynamic simulations are often prohibitively expensive at scales of interest. Prior studies have therefore utilized low-order effective medium models, such as discrete mass-spring lattices, to capture essential properties in the long-wavelength limit. While models of this type have been successfully implemented for a wide variety of nonlinear elastic systems, they have predominantly considered dynamics depending only on the instantaneous kinematics of the lattice, neglecting history-dependent effects, such as wear and plasticity. Here, to address this limitation, the present study develops a lattice-based modeling framework for nonlinear elastic metamaterials undergoing plastic deformation. Due to the history- and rate-dependent nature of plasticity, the framework generally yields a system of differential-algebraic equations whose computational cost is significantly greater than an elastic system of comparable size. We demonstrate the method using several models inspired by classical lattice dynamics and continuum plasticity theory and explore means to obtain empirical plasticity models for general geometries, thereby gaining insight into the influence of microstructural plasticity on effective material performance, which can be used to improve the design of nonlinear mechanical metamaterials.

Dynamic simulation↗

NASA NTRSDate not supplied

Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS): Robotically Assembled Sustainable Lunar Infrastructure

Introduction: The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project at NASA Ames Research Center is developing autonomous infrastructure, instrumentation, and spacecraft assembly and manufacturing capabilities for next generation exploration and science missions, with a goal to change the cost scaling of these missions relative to mission size and duration. Using a building-block approach with a 'kit of parts' composed of ultra-light, high-performance mechanical metamaterials, simplified robots leverage the period environment to achieve high levels of autonomy and reliability for in-space and surface assembly of large-scale apertures, solar-arrays, towers, habitats, and other infrastructure. Robots and structure break down into a compact form factor for launch. By leveraging economies of scale and achieving high-packing ratios, ARMADAS technology can revolutionize possible space missions by breaking the tyranny of the launch shroud, decreasing development times, decreasing mission costs for transformative science capability, and providing a scalable and versatile space infrastructure strategy. Capabilities: To date, the ARMADAS project has demonstrated autonomous assembly of large numbers of structural modules into a meters-scale structure in an earth gravity environment. The mechanical performance is on par with conventional space structures, at a fraction of the cost. By using highly repeatable manufacturing processes (injection molding carbon fiber reinforced space-rated polymers), the structures are both cost effective and highly precise. The ARAMADAS system carefully designs parts for high-precision assembly–the simple and cost-effective robots build structures much larger and more precise than themselves. While maintaining structural efficiency, the ARMADAS system encompasses many functional module types, including for solar power, comm/power routing, etc., that will enable entire infrastructure systems to be engineered and autonomously assembled with modular parts. With well-defined interfaces, custom instrumentation modules can easily be added to fit many mission objectives. Lunar Infrastructure Vision: At the LSIC spring meeting, ARMADAS will present a vision for a general-purpose lunar construction kit capable of meeting a wide variety of lunar surface infrastructure needs (Figure 1). Investing in an ecosystem of reconfigurable, discretely repairable parts and robots enables systems than can expand their capability, reconfigure to meet emergency or unforeseen needs, self-repair and reduce spare-part needs. With a small set of parts (module types), a wide variety of infrastructure needs can be met. Footing modules will allow infrastructure construction at locations with no surface preparation. Footer, primary structure, and rail modules can create reconfigurable rail systems. These rail systems can be used to reduce cost-of-transport between frequently accessed sites, provide dust mitigation, and convey power and communications. High-performance structure modules combined with power-routing and solar modules can create tall towers for power and communication. Concepts for habitats and garages encompass a structure to support regolith cover for a pre-integrated module to an entirely ARMADAS system-based structure. This system can be leveraged by many in-situ resource utilization (ISRU) technologies to simplify processes and augment capabilities.

C. E. Gregg↗

DOE OSTI2025

Vibration mitigation in interlocking metasurfaces

Interlocking metasurfaces (ILMs) are a new class of mechanical metamaterials with patterned surface features that enable joining of two adjacent bodies, serving as an alternative to welds, fasteners, or adhesives. While the strength of ILMs has been explored previously, it is also possible to envision ILMs that possess the ability to dampen vibration or tailor energy transmission across the interface. In this study, we explore the vibration transmission under frequency sweep and random excitation of three ILM designs. This is the first study to characterize the response of ILMs under vibration. We find that ILMs which are tightly coupled (e.g., small interference fit) behave as a linear system and depart only marginally from the behavior of a solid body. However, through strategic manipulation of the ILM topology, and in particular the fit clearances, we show that vibrations can be damped by over an order of magnitude. Design strategies to employ ILMs for vibration mitigation are developed using a first-order model.

Bolmin, O. [Carnegie Mellon Univ., Pittsburgh, PA ↗

DOE OSTI2025

3D nanolithography with metalens arrays and spatially adaptive illumination

The growing demand for advanced materials, miniaturized devices and integrated microsystems calls for the reliable fabrication of complex, multiscale, three-dimensional (3D) architectures, a need increasingly addressed through light-based and laser-based processes. However, owing to the field-of-view (FOV) limitations of conventional imaging optics, existing 3D laser nanofabrication techniques face fundamental challenges in throughput, proximity error and stitching defects on the path to scaling. Here, in this study, we present a scalable 3D nanofabrication platform that uses a metalens-generated focal spot array to parallelize two-photon lithography (TPL) beyond centimetre-scale write field areas. Metalenses are ideally suited for producing submicron-scale focal spots for high-throughput nanolithography, as they uniquely feature large numerical apertures (NAs), immersion media compatibility and large-scale manufacturability. We experimentally demonstrate a printing system that uses a 12-cm 2 metalens array to produce more than 120,000 cooperative focal spots, corresponding to a throughput exceeding 10 8 voxels s −1 . By programmatically patterning the focal spot array using a spatial light modulator (SLM), an adaptive parallel printing strategy is developed for precise greyscale linewidth modulation and choreographed printing of semiperiodic and fully aperiodic 3D geometries. We demonstrate parallel printing of replicated microstructures (>50 M microparticles per day), centimetre-scale 3D architectures with feature sizes down to 113 nm, and photonic and mechanical metamaterials. This work demonstrates the potential of 3D nanolithography towards wafer-scale production, showing how TPL could be used at scale for applications in microelectronics, biomedicine, quantum technology and high-energy laser targets.

Materials science↗

DOE OSTI2026

T3tris: AI-Driven Inverse Design of Cellular Materials

Los Alamos National Laboratory has developed T3tris, a generative AI platform that enables real-time inverse design of architected cellular materials. This capability transforms how mechanical metamaterials are designed by generating microstructural topologies that conform to target nonlinear stress-strain curves, even at high compressive strain (up to 50%).

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Autonomous Lunar Infrastructure Outfitting

The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project at NASA Ames Research Center is developing autonomous infrastructure, instrumentation, and spacecraft assembly and manufacturing capabilities for next generation exploration and science missions, with a goal to change the cost scaling of these missions relative to mission size and duration. Using a building-block approach with a 'kit of parts' composed of ultra-light, high-performance mechanical metamaterials, simplified robots leverage the period environment to achieve high levels of autonomy and reliability for in-space and surface assembly of large-scale apertures, solar-arrays, towers, habitats, and other infrastructure. Robots and structure break down into a compact form factor for launch. By leveraging economies of scale and achieving high-packing ratios, ARMADAS technology can revolutionize space missions by breaking the tyranny of the launch shroud, decreasing development times, decreasing mission costs for transformative science capability, and providing a scalable and versatile space infrastructure strategy. An ecosystem of reconfigurable infrastructure modules can be reused, repaired, expanded reconfigured to meet emergency or unforeseen needs, and reduce spare parts.

Christine Gregg↗

DOE OSTI2024

An optimization-based approach to tailor the mechanical response of soft metamaterials undergoing rate-dependent instabilities

An optimization-based design framework is proposed to tune the response of soft metamaterials involving both geometric instabilities and nonlinear viscoelastic material behavior. Designing the response of soft metamaterials to harness instabilities and undergo large, tailored configuration changes will enable advancements in soft robotics, shock and vibration mitigation, and flexible electronics. In line with the metamaterial concept, the response of these materials is governed to a large extent by the geometric and topological makeup of their small-scale features. However, the link between structure and response is less intuitive for soft metamaterials due to their reliance upon highly nonlinear responses triggered by geometric instabilities. This is further complicated by the effects of viscoelastic relaxation, which recent studies have shown to alter the emergence of instabilities in non-intuitive ways. Here, these effects are accounted for in our framework to achieve various design objectives, including tailored force–displacement response and maximized energy absorption from both geometric and material effects. To fully automate this process, it is essential to have a completely robust equation solver for forward problems involving instabilities and viscoelastic relaxation. We achieve this by casting the search for stable mechanical equilibrium — i.e. the forward problem — as a minimization problem and utilize a trust region algorithm to robustly handle instabilities and follow energetically-favorable equilibrium paths through critical points.

97 MATHEMATICS AND COMPUTING↗