An official website of the United States government
We envision programmable matters that can alter their physical properties in desirable manners based on user input or autonomous sensing. This vision motivates the pursuit of mechanical metamaterials that interact with the environment in a programmable fashion. However, this has not been systematically achieved for soft metamaterials because of the highly nonlinear deformation and underdevelopment of rational design strategies. Here, we use computational morphogenesis and multimaterial polymer 3D printing to systematically create soft metamaterials with arbitrarily programmable temperature-switchable nonlinear mechanical responses under large deformations. This is made possible by harnessing the distinct glass transition temperatures of different polymers, which, when optimally synthesized, produce local and giant stiffness changes in a controllable manner. Featuring complex geometries, the generated structures and metamaterials exhibit fundamentally different yet programmable nonlinear force-displacement relations and deformation patterns as temperature varies. The rational design and fabrication establish an objective-oriented synthesis of metamaterials with freely tunable thermally adaptive behaviors. This imbues structures and materials with environment-aware intelligence.
more »
« less
Computational morphogenesis for liquid crystal elastomer metamaterial
Abstract Liquid crystal elastomer (LCE) is a type of soft active material that generates large and reversible spontaneous deformations upon temperature changes, facilitating various environmentally responsive smart applications. Despite their success, most existing LCE metamaterials are designed in a forward fashion based on intuition and feature regular material patterns, which may hinder the reach of LCE’s full potential in producing complex and desired functionalities. Here, we develop a computational inverse design framework for discovering diverse sophisticated temperature-activated and -interactive nonlinear behaviors for LCE metamaterials in a fully controllable fashion. We generate intelligent LCE metamaterials with a wide range of switchable functionalities upon temperature changes. By sensing the environment, these metamaterials can realize maximized spontaneous area expansion/contraction, precisely programmable enclosed opening size change, and temperature-switchable nonlinear stress–strain relations and deformation modes. The optimized unit cells feature irregular LCE patterns and form complex and highly nonlinear mechanisms. The inverse design computational framework, optimized material patterns, and revealed underlying mechanisms fundamentally advance the design capacity of LCE metamaterials, benefiting environment-aware and -adaptive smart materials.
more »
« less
- PAR ID:
- 10514964
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance. While their elastic mechanical properties have been systematically determined, their large-deformation, nonlinear responses have been challenging to predict and design, in part due to limited data sets and the need for complex nonlinear simulations. This work presents a novel physics-enhanced machine learning (ML) and optimization framework tailored to address the challenges of designing intricate spinodal metamaterials with customized mechanical properties in large-deformation scenarios where computational modeling is restrictive and experimental data is sparse. By utilizing large-deformation experimental data directly, this approach facilitates the inverse design of spinodal structures with precise finite-strain mechanical responses. The framework sheds light on instability-induced pattern formation in spinodal metamaterialsobserved experimentally and in selected nonlinear simulations—leveraging physics-based inductive biases in the form of nonconvex energetic potentials. Altogether, this combined ML, experimental, and computational effort provides a route for efficient and accurate design of complex spinodal metamaterials for large-deformation scenarios where energy absorption and prediction of nonlinear failure mechanisms is essential.more » « less
-
There is growing interest in engineering uncon- ventional computing devices that leverage the in- trinsic dynamics of physical substrates to perform fast and energy-efficient computations. Granu- lar metamaterials are one such substrate that has emerged as a promising platform for building wave-based information processing devices with the potential to integrate sensing, actuation, and computation. Their high-dimensional and non- linear dynamics result in nontrivial and some- times counter-intuitive wave responses that can be shaped by the material properties, geometry, and configuration of individual grains. Such highly tunable rich dynamics can be utilized for mechan- ical computing in special-purpose applications. However, there are currently no general frame- works for the inverse design of large-scale granu- lar materials. Here, we build upon the similarity between the spatiotemporal dynamics of wave propagation in material and the computational dy- namics of Recurrent Neural Networks to develop a gradient-based optimization framework for har- monically driven granular crystals. We showcase how our framework can be utilized to design basic logic gates where mechanical vibrations carry the information at predetermined frequencies. We compare our design methodology with classic gradient-free methods and find that our approach discovers higher-performing configurations with less computational effort. Our findings show that a gradient-based optimization method can greatly expand the design space of metamaterials and pro- vide the opportunity to systematically traverse the parameter space to find materials with the desired functionalities.more » « less
-
Abstract The properties of materials and structures typically remain fixed after being designed and manufactured. There is a growing interest in systems with the capability of altering their behaviors without changing geometries or material constitutions, because such reprogrammable behaviors could unlock multiple functionalities within a single design. We introduce an optimization-driven approach, based on multi-objective magneto-mechanical topology optimization, to design magneto-active metamaterials and structures whose properties can be seamlessly reprogrammed by switching on and off the external stimuli fields. This optimized material system exhibits one response under pure mechanical loading, and switches to a distinct response under joint mechanical and magnetic stimuli. We discover and experimentally demonstrate magneto-mechanical metamaterials and metastructures that realize a wide range of reprogrammable responses, including multi-functional actuation responses, adaptable snap-buckling behaviors, switchable deformation modes, and tunable bistability. The proposed approach paves the way for promising applications such as magnetic actuators, soft robots, and energy harvesters.more » « less
-
This work is concerned with inverse design of the grating metasurface over hyperbolic metamaterials (HMMs) in order to enhance spontaneous emission (SE). We formulate the design problem as a PDE-constrained optimization problem and employ the gradient descent method to solve the underlying optimization problem. The adjoint-state method is applied to compute the gradient of the objective function efficiently. Computational results show that the SE efficiency of the optical structure with the optimized metasurface increases by 600% in the near field compared to the bare HMM layer. In particular, an optimized double-slot metasurface obtained by this design method enhances the SE intensity by a factor of over 100 in the observation region.more » « less
