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3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D graphene sheets, showing excellent performances in applications such as mechanical support, energy storage, and electrochemical catalysis. However, given the diversity and complexity of possible graphene 3D structures, there does not yet exist a systematic approach that can generate target 3D shapes and also, evaluate their performance. Here high‐throughput data generation is combined with artificial intelligence approaches to realize rapid structure formation and property quantification of 3D graphene foams with mathematically controlled topologies, driven by molecular dynamics simulations. More than 4000 different foam structures are created, which feature diverse topologies that contain potential pathways for small molecules and auxetic structures with negative Poisson's ratio. Empowered by machine learning (ML) algorithms including graph neural networks, not only global properties such as elastic moduli, but also local behaviors such as atomic stress can be predicted and optimized based on their atomic structure, bypassing expensive atomistic simulations. The key findings of the research reported in this paper include a high‐throughput virtual framework of generating diverse 3D graphene assemblies with mechanical performances quantification, and highly efficient methods of evaluating physical properties based on ML. [ABSTRACT FROM AUTHOR]

This paper is devoted to a category of metamaterials called auxetics, identified by their negative Poisson's ratio. Our work consists in exploring geometrical strategies to generate irregular auxetic structures. More precisely we seek to reduce the Poisson's ratio ν, by pruning an irregular network based solely on geometric criteria. We introduce a strategy combining a pure geometric pruning algorithm followed by a physics‐based testing phase to determine the resulting Poisson's ratio of our structures. We propose an algorithm that generates sets of irregular auxetic networks. Our contributions include geometrical characterization of auxetic networks, development of a pruning strategy, generation of auxetic networks with low Poisson's ratio, as well as validation of our approach. We provide statistical validation of our approach on large sets of irregular networks, and we additionally laser‐cut auxetic networks in sheets of rubber. The findings reported here show that it is possible to reduce the Poisson's ratio by geometric pruning, and that we can generate irregular auxetic networks at lower processing times than a physics‐based approach. [ABSTRACT FROM AUTHOR]

Recent frontiers in material development are represented by a class of so-called auxetic metamaterials that, thanks to their structure rather than composition, are characterized by a negative Poisson's ratio. In the present paper a two-dimensional auxetic plate, made by structural straight elements forming a lattice periodic structure with re-entrant cells, is considered. A thorough discussion on the linear and geometrically nonlinear deformability of the auxetic plate is presented. The key geometric parameters governing the deformability of the plate are identified, and some analytical expressions for calculating the Poisson's ratio, as a function of the applied strain, are given. Numerical (finite element) analyses and experimental tests on 3D printed specimens are carried out to verify the theoretical findings. For the latter ones, full field strain maps are obtained by means of a suitable interpolation of the sampled displacement field measured by digital image techniques. [ABSTRACT FROM AUTHOR]

Transformation acoustics opens a new avenue towards the fabrication of a new class of sonic composites with scatterers made of auxetic materials embedded in the epoxy matrix. The design of the acoustic scatterers is based on the property of Helmoltz equations to be invariant under coordinate transformations, i.e. a specific spatial compression is equivalent to a variation of the material parameters in the original space. In this paper, the noise suppression for a wide full band-gap of frequencies is discussed for spherical shell scatterers made of auxetic materials. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]