Kon‐Well Wang

Modern autonomous systems are driving the critical need for next-generation adaptive materials and structures with embodied intelligence, i.e., the embodiment of memory, perception, learning, and decision-making within the mechanical domain. A fundamental challenge is the seamless and efficient integration of memory with information processing in a physically interpretable way that enables cognitive learning and decision-making under uncertainty. Prevailing paradigms, from intricate logic cascades to black-box morphological computing or physical neural networks, are seriously limited by trade-offs among efficiency, scalability, interpretability, transparency, and reliance on additional electronics. Here, we introduce in-memory phononic learning, a paradigm-shifting framework that unifies nonvolatile mechanical memory with wave-based perception within a phononic metastructure. Our system encodes spatial information into stable structural states as mechanical memory that directly programs its elastic wave-propagation landscape. This memory/wave-dynamics coupling enables effective sensory perception, decomposing complex patterns into informative geometric features through frequency-selective wave localization. Learning is created by optimizing input waveforms to selectively probe these features for memory-pattern classification, with decisions inferred directly from the output wave energy, thereby completing the entire information loop mechanically through an efficient and physically transparent mechanism without hidden architectures or electronics. This work transcends the paradigm of 'materials that compute' to cognitive matter capable of interpreting dynamic environments, paving the way for future intelligent structural-material systems with low power consumption, more direct interaction with surroundings, and enhanced cybersecurity and resilience in harsh conditions.

Abstract This is a roadmap article with multiple contributors on different aspects of embodying intelligence and computing in the mechanical domain of functional materials and structures. Overall, an IOP roadmap article is a broad, multi-author review with leaders in the field discussing the latest developments, commissioned by the editorial board. The intention here is to cover various topics of adaptive structural and material systems with mechano-intelligence in the overall roadmap, with twelve sections in total. These sections cover topics from materials to devices to systems, such as computational metamaterials, neuromorphic materials, mechanical and material logic, mechanical memory, soft matter computing, physical reservoir computing, wave-based computing, morphological computing, mechanical neural networks, plant-inspired intelligence, pneumatic logic circuits, intelligent robotics, and embodying mechano-intelligence for engineering functionalities via physical computing. In this paper, we view all the sections with equal contributions to the overall roadmap article and thus list the authorship on the front page via alphabetical order of their last names. On the other hand, for each individual section, the authors decide on their own the order of authorship. (Abstract written by Guest Editors Kon-Well Wang (aka K W Wang) and Suyi Li.)

The unique phenomena arising from Dirac cones in phononic crystals offer promising avenues for on-demand sound manipulations with compelling applications in low-power acoustic sensing and imaging. However, the practical utilization of Dirac cone--enabled phononic crystals in engineering systems is limited by a lack of significant Dirac cone tunability. In this research, we investigate an easy-to-implement method to control Dirac cones via continuous phononic lattice symmetry manipulation. For the first time, we systematically investigate and uncover how the changes in the lattice symmetry, perturbations from its perfectly symmetric configurations, alter the phononic crystal band structure, and we utilize this insight to introduce a tunable phononic crystal with Dirac cones continuously modulated to significantly change their position in the band diagram. We demonstrate experimentally how this tunability feature is leveraged to design beamformers able to generate highly directive sound beams and steer them over a wide range of angles. The single-transducer and single-degree-of-freedom actuation make the device remarkably low power and easy to realize, making it ideal for the development of practical acoustic imaging and sensing systems. Moreover, this work opens up exciting paths forward and prospects for the design and utilization of phononic crystals beyond the confines of traditional perfectly symmetric configurations.

Recently, researchers have incorporated topological phases into mechanical metamaterials to facilitate defect-immune elastic wave and vibration manipulation. The topological mechanical metamaterials developed thus far have achieved extraordinary wave control capabilities through the construction of robust elastic waveguides at the boundaries and interfaces of 1D, 2D, and 3D periodic mechanical lattices. Given the overwhelming focus of previous research on traditional integer-dimensional mechanical architectures, an unexplored opportunity exists to investigate the emergence of topological phases in fractal mechanical metamaterials, which have a non-integer dimension and exhibit self-similarity across multiple scales. This research addresses the unexplored opportunity and advances the state of the art through the synthesis of a 1.89D fractal mechanical metamaterial that harnesses higher-order topological phases to enable multifaceted elastic wave and vibration control. The proposed fractal topological mechanical metamaterial is a thin plate with embedded torsional spring-mass resonators that are arranged into the pattern of a 1.89D Sierpiński carpet. A numerical eigenfrequency analysis uncovers coexisting topological corner and edge states that trap wave energy at the myriad corner and edge interfaces available in the 1.89D fractal. The outcomes from this study provide insight into the attainment of higher-order topological states in fractal metamaterials that localize elastic waves and vibrations across various locations and frequencies, opening the door for future research of topological phases in mechanical metamaterials with fractal architectures.

In recent years, the concept of reconfigurable matter engineered based on nature-inspired modular architectures has been explored to create advanced structures. The modules are designed to be reconfigurable, so to produce synergistic and intriguing dynamic functionalities, such as programmable phononic bandgap and nontraditional wave steering. More recently, with the rapid advances in high-performance intelligent systems, we are witnessing a prominent demand for the next generation of mechanical matter to have much more built-in intelligence and autonomy. An emerging direction is to pioneer the structures’ high dimensionality and nonlinearity for mechano-intelligence via physical computing. That is, we aim to concurrently embody computing power and functional intelligence, such as perception, learning, memorizing, decision-making and execution, directly in the mechanical domain, advancing from conventional systems that solely rely on add-on digital computer to achieve intelligence. This presentation will highlight some of these advancements in harnessing engineered matter for structural dynamics tailoring, from adaptive wave controls to self-learning-self-tuning intelligence.

In this study, we introduce an innovative approach employing magnetically induced multistable metamaterials to effectively realize programmable elastic waveguides. The designed structure incorporates segregated layers for multistability, allowing for efficient state reconfiguration to realize adaptable elastic waveguiding utilizing defect modes, minimizing the damping effects in traditional multistable metamaterials. Through systematic numerical simulations and experimental validations, we evaluate the system’s on-demand capability to program elastic wave propagation paths and their operating frequency ranges using the proposed metamaterial. This research offers new practical means for engineering structural systems with enhanced reconfigurability and highly adaptable, broadband wave manipulations. It also opens a door to achieving future intelligent metamaterial systems with stable mechanical memory and wave information processing capabilities.

Mechano-Intelligence In article number 2305074, Yuning Zhang, Aditya Deshmukh, and Kon-Well Wang introduce an innovative method for embodying multifunctional mechano-intelligence into phononic metastructures, harnessing their wave dynamics as computational resources through a physical-body-based analog neural network. This breakthrough lays the foundation for the development of future intelligent structures that can perceive information, learn, make decisions, and execute actions within their mechanical domain.

Mechanical Learning In article number 2305146, Zuolin Liu, Hongbin Fang, Jian Xu, and Kon-Well Wang propose an innovative approach for mechanical learning through multistable origami metamaterials. Inspired by cellular automata, the multistable transition sequences are served as computing resources in the framework of reservoir computing. Digit recognition and complex computation tasks are successfully implemented through experiments by a single actuator, revolutionizing the potential for intelligent materials in diverse applications from computational science to mechano-intelligence.

Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing is proposed, which can accomplish high-level computation tasks without the need to construct a logic gate network. This approach thus eliminates the demanding requirements for the fabrication of materials and signal propagation when constructing large-scale networks for high-level computation in conventional mechanical logic. Using the multistable stacked Miura-origami metamaterial as a validation platform, digit recognition is experimentally implemented by a single actuator. Moreover, complex tasks, such as handwriting recognition and 5-bit memory tasks, are also shown to be feasible with the new computation framework. The research represents a significant advancement in developing a new generation of intelligent materials with advanced computational capabilities. With continued research and development, these materials can have a transformative impact on a wide range of fields, from computational science to material mechano-intelligence technology and beyond.

Recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess built-in intelligence in their mechanical domain, the so-called mechano-intelligence (MI). Previous MI attempts mainly focused on specific case studies and lacked a systematic foundation in effectively and efficiently constructing and integrating different intelligent functions. Here, a new approach is uncovered to create multifunctional MI in adaptive structures using physical reservoir computing (PRC). That is, to concurrently embody computing power and the key elements of intelligence, namely perception, decision-making, and commanding, directly in the mechanical domain, advancing from conventional reliance on add-on computers and massive electronics. As an exemplar platform, a mechanically intelligent phononic metastructure is developed by harnessing its high-degree-of-freedom nonlinear dynamics as PRC power. Through analyses and experiments, multiple intelligent structural functions are demonstrated ranging from self-tuning wave controls to wave-based logic gates. This research provides the much-needed basis for creating future smart structures and materials that greatly surpass the state of the art-such as lower power consumption, more direct interactions, and better survivability in harsh environments or under cyberattacks. Moreover, it enables the addition of new functions and autonomy to systems without overburdening the onboard computers.

Topological mechanical metamaterials unlock confined and robust elastic wave control. Recent breakthroughs have precipitated the development of 3D topological metamaterials, which facilitate extraordinary wave manipulation along 2D planar and layer-dependent waveguides. The 3D topological metamaterials studied thus far are constrained to function in single-frequency bandwidths that are typically in a high-frequency regime, and a comprehensive experimental investigation remains elusive. In this paper, these research gaps are addressed and the state of the art is advanced through the synthesis and experimental realization of a 3D topological metamaterial that exploits multimodal local resonance to enable low-frequency elastic wave control over multiple distinct frequency bands. The proposed metamaterial is geometrically configured to create multimodal local resonators whose frequency characteristics govern the emergence of four unique low-frequency topological states. Numerical simulations uncover how these topological states can be employed to achieve polarization-, frequency-, and layer-dependent wave manipulation in 3D structures. An experimental study results in the attainment of complete wave fields that illustrate 2D topological waveguides and multi-polarized wave control in a physical testbed. The outcomes from this work provide insight that will aid future research on 3D topological mechanical metamaterials and reveal the applicability of the proposed metamaterial for wave control applications.

Abstract In order to apply the high nitrogen-content tetrazole derivatives (TZD), a class of energy materials with great potential, into gun propellant, we propose a novel idea that grafting the acidized TZD onto cellulose and then nitrifying the grafted cellulose to obtain nitrocellulose grafted by TZD (TZDNC). Theoretical predictions suggest that Replacing nitrocellulose (NC) with TZDNC, due to having higher nitrogen-content of TZDNC than traditional NC, can evidently reduce the ablation to gun tube, smoke and flame around muzzle of gun tube. The replacement not only solves the incompatibility of TZD with the other components such as NG, RDX and HMX, but maintains the nearly gun powder power of gun propellant before replacing.

Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, we propose a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing, which can accomplish high-level computation tasks without the need to construct a logic gate network. This approach thus eleimates the demanding requirements for fabrication of materials and signal propagation when constructing large-scale networks for high-level computation in conventional mechano-logic. Using the multistable stacked Miura-origami metamaterial as a validation platform, digit recognition is successfully implemented through experiments by a single actuator. Moreover, complex tasks, such as handwriting recognition and 5-bit memory tasks, are also shown to be feasible with the new computation framework. Our research represents a significant advancement in developing a new generation of intelligent materials with advanced computational capabilities. With continued research and development, these materials could have a transformative impact on a wide range of fields, from computational science to material mechano-intelligence technology and beyond.

Tensegrity structures have been harnessed in the design of many reconfigurable and deployable systems due to their high strength to weight ratio, stiffness tunability and multistability programmability. In this paper, we present a design methodology of an origami-inspired multistable tensegrity structure that can achieve up to three stable configurations in one unit cell. This class-3 tensegrity structure can achieve equilibrium states at the fully deployed and flat folded states, and the transition between its stable states is controlled with a one directional displacement, a feature not observed in previous tensegrity elements. To design this system the required input are the three heights at which a stable configuration is desired. At each height the total strain energy of the system of strings is evaluated to select the unstretched length and stiffness values of each string that satisfy the conditions of stability. Analytically it was found that achieving a stable configuration at each height is affected by the number of strings that are in tension at this point, and the deformation path, stiffness and unstretched length of each string.

This research experimentally investigates the integration of mechano-intelligence into mechanical metastructures for self-adaptive wave control. We created a phononic metastructure prototype utilizing periodic buckled beam modules that has highly adjustable wave propagation characteristics via length reconfiguration using a linear displacement actuator. By utilizing the physical reservoir computing framework, we show that the proposed metastructure can recognize and self-adapt to different inputs by making decisions on appropriate actuations to reconfigure itself to achieve an intelligent wave blocking task. Overall, this research provided a promising approach for constructing and integrating functional mechano-intelligence in structures harnessing physical computing and learning, and created a new direction for the next generation of adaptive structures and material systems.