Computational design of metamaterials

This issue of Nature Computational Science features a Focus that highlights recent advancements, challenges, and opportunities in computational models for metamaterials design and manufacturing, as well as explores their potential promises in emerging information processors and computing technologies.

Optical and wave-based computing is attracting renewed interest, motivated by the need for new platforms for resource-intensive special-purpose processing tasks. Here, we discuss whether, why, and how metamaterials and metasurfaces could contribute to achieving an ‘optical advantage’ in computing.

In recent years, there has been a surge of interest in the design of mechanical metamaterials for different science and engineering applications. In particular, various computational approaches have been developed to facilitate the systematic design of art-inspired metamaterials including origami and kirigami metamaterials. In this Comment, we highlight the recent advances and discuss the outlook for the computational design of art-inspired metamaterials.

Additive manufacturing plays an essential role in producing metamaterials by precisely controlling geometries and multiscale structures to achieve the desired properties. In this Comment, we highlight the challenges and opportunities from additive manufacturing for computational metamaterials design.

Dr Yongmin Liu — professor of mechanical and industrial engineering and professor of electrical and computer engineering at Northeastern University — talks to Nature Computational Science about his career trajectory, his research on photonic metamaterials, and the synergistic effects between photonic metamaterials research and artificial intelligence (AI).

Mechanical metamaterials have shown potential for processing information via autonomous environmental interactions. This Perspective summarizes recent efforts and challenges on integrating stimuli-responsive materials with mechanical metamaterials for mechanical computing, and explores the remaining challenges in the field.

Computational tools have recently empowered mechanical metamaterials design. In this Perspective, advances to these approaches are discussed, notably mechanism-based design, topology optimization, the use of machine learning and the challenges for additive-manufactured metamaterial structures.

Here a conformality-assisted tracing method is proposed to devise free-form and three-dimensional conformal metamaterials, featuring accuracy and efficiency in handling complex geometry and adaptability to various diffusion and wave fields.

A recent study proposes a computational method for the design of free-form metamaterials systems. The method simplifies the design process by avoiding the use of anisotropic materials that are usually required for the conventional methods. The method can be applied in designing both two-dimensional and three-dimensional metamaterials that are subject to multiple physical fields.

Partial differential equations are typically solved on every element of a discretization basis before extracting the desired information, and each input requires one solution. In this study, a strategy is proposed to directly compute the quantities of interest, bypassing full-basis solutions and avoiding repetition over inputs.

A method for making large-scale nanophotonic simulations more computationally efficient is proposed, enabling a wide range of studies to be less time- and memory-intensive.

Optical computing has the potential to be faster and more energy-efficient than conventional digital-electronic computing for certain applications. This Perspective article surveys the differences between optics and electronics that could be exploited, and explores the physics and engineering challenges in realizing useful optical computers.

Colloidal nanocrystals can form into periodic superlattices exhibiting collective vibrations from the correlated motion of the nanocrystals. This Perspective discusses such collective vibrations and their as-of-yet untapped potential applications for phononic crystals, acoustic metamaterials and optomechanical systems.

Recent advances in optical metalenses are reviewed with a focus on their unique features and applications in the space of optical metasystems.

A single-metasurface-based holographic light projection covering the whole 360° field of view is realized by optimizing the metasurface design through a neural network and applying 360° structured light for holographic light projection and three-dimensional imaging.

Researchers demonstrate a compact metasurface-based Mueller matrix imaging system. All 16 components of an object’s spatially varying Mueller matrix can be attained in a single shot.

Using inverse design, a 3D silicon photonics platform that can be used for the mathematical operation of vector–matrix multiplication with light is demonstrated, potentially enabling large-scale wave-based analogue computing.

Kirigami is an ancient art form that is now increasingly studied and applied in science and technology. This work presents an additive approach for the computational design of kirigami and two fabrication strategies for its physical instantiation.

A computational tool based on an additive approach and linear algebra has been developed together with a fabrication strategy for the systematic exploration of rigid-deployable, compact and reconfigurable kirigami patterns.

Mechanical metamaterials are known for their unconventional mechanical properties. In this perspective, the authors give an overview of the current state of mechanical materials research and suggest a roadmap for next-generation active and responsive mechanical metamaterials.

Computing approaches based on mechanical mechanisms are discussed, with a view towards a framework in which adaptable materials and structures act as a distributed information processing network.

Truss metamaterials are ubiquitous but their vast design space is far from fully explored. Here, authors use machine learning to present a unified, continuous latent space description, enabling the rapid generation of trusses with tunable or exceptional linear and nonlinear mechanical properties.

Mechanical behavior of a material is captured by a measured stress-strain curve upon loading. Here, the authors report a rapid inverse design methodology via machine learning and 3D printing to create metamaterials with mechanical behavior that replicates a user-prescribed stress-strain curve.