Viacheslav Slesarenko’s research while affiliated with University of Freiburg and other places

Generative machine learning models have shown notable success in identifying architectures for metamaterials—materials whose behavior is determined primarily by their internal organization—that match specific target properties. By examining kirigami metamaterials, in which dependencies between cuts yield complex design restrictions, we demonstrate that this perceived success in the employment of generative models for metamaterials might be akin to survivorship bias. We assess the performance of the four most popular generative models—the Variational Autoencoder (VAE), the Generative Adversarial Network (GAN), the Wasserstein GAN (WGAN), and the Denoising Diffusion Probabilistic Model (DDPM)—in generating kirigami structures. Prohibiting cut intersections can prevent the identification of an appropriate similarity measure for kirigami metamaterials, significantly impacting the effectiveness of VAE and WGAN, which rely on the Euclidean distance—a metric shown to be unsuitable for considered geometries. This imposes significant limitations on employing modern generative models for the creation of diverse metamaterials.

Self‐sealing is one of the fascinating functions in nature that enables living material systems to respond immediately to damage. A prime plant model is Delosperma cooperi, which can rapidly self‐seal damaged succulent leaves by systematically deforming until the wound closes. Inspired by this self‐sealing principle, a novel programmable mechanical metamaterial has been developed to mimic the underlying damage management concept. This material is able to react autonomously to changes in its physical condition caused by an induced damage. To design this ability into the programmable metamaterial, a permeable unit cell design has been developed that can change size depending on the internal pressure. The parameter space and associated mechanical functionality of the unit cell design is simulated and analyzed under periodic boundary conditions and various pressures. The principles of self‐sealing behavior in designed metamaterials are investigated, crack closure efficiency is identified for different crack lengths, the limitations of the proposed approach are discussed, and successful crack closure is experimentally demonstrated in the fabricated metamaterial. Although this study facilitates the first step on the way of integrating new bio‐inspired principles in the metamaterials, the results show how programmable mechanical metamaterials might extend materials design space from pure properties to life‐like abilities.

The emergence of additive manufacturing has enabled the fabrication of architected materials with intricate micro- and nanoscale features. However, each fabrication method has a specific minimum feature size that can be practically achieved. As engineers pursue lightweight and high-performance materials, the elements of these architected materials often approach this minimum feature size, which poses a risk to their structural integrity. The failure of individual struts can result in the complete breaking of the lattice metamaterial’s connectivity or, depending on the internal architecture, only a marginal reduction in its load-bearing capacity. In this short letter, we use a minimal viable unit cell to demonstrate how an anisotropic lattice, constructed with beams of varying thicknesses, can surpass a lattice consisting solely of uniform thickness beams in terms of damage tolerance. Our focus is primarily on the manufacturing limitations rather than defects that may arise during the loading of architected materials. We propose an approach where the probability of each individual strut failure depends on its thickness, and we illustrate the implications using a simple step-like function. This approach can be extended to more complex metamaterials or to explore intricate relationships between failure probability and beam thickness.

The frictional properties of material interfaces can be rationally designed

This Letter discusses elastic metamaterials incorporating curved beams in their architecture. Through employing Bezier splines, we reveal a wide versatility of geometrical designs of the unit cells and the consequent programmability of bandgap structures. By analyzing more than ten thousand possible specimens altogether, we highlight the similarity between dynamic properties of metamaterials formed by curves with different geometries defined via three variables only that correspond to the coordinates of control points of the Bezier spline. In particular, we establish the importance of such parameter as effective curve length in defining the probable positions of bandgaps. This study shows, in particular, that the bandgap ratio can reach 71% for metamaterials with proposed curved beams—a noticeable contrast with no bandgaps in their counterpart with straight elements. The employment of the deep learning model enables us to effectively predict passband–stopband structure in such metamaterials with satisfactory accuracy, potentially accelerating the design of metamaterials assembled from versatile unit cells.

During the recent decade, we have witnessed an extraordinary flourishing of soft robotics. Rekindled interest in soft robots is partially associated with the advances in manufacturing techniques that enable the fabrication of sophisticated multi-material robotic bodies with dimensions ranging across multiple length scales. In recent manuscripts, a reader might find peculiar-looking soft robots capable of grasping, walking, or swimming. However, the growth in publication numbers does not always reflect the real progress in the field since many manuscripts employ very similar ideas and just tweak soft body geometries. Therefore, we unreservedly agree with the sentiment that future research must move beyond “soft for soft’s sake.” Soft robotics is an undoubtedly fascinating field, but it requires a critical assessment of the limitations and challenges, enabling us to spotlight the areas and directions where soft robots will have the best leverage over their traditional counterparts. In this perspective paper, we discuss the current state of robotic research related to such important aspects as energy autonomy, electronic-free logic, and sustainability. The goal is to critically look at perspectives of soft robotics from two opposite points of view provided by early career researchers and highlight the most promising future direction, that is, in our opinion, the employment of soft robotic technologies for soft bio-inspired artificial organs.