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Flexoelectricity is a form of electromechanical coupling that has recently emerged because, unlike piezoelectricity, it is theoretically possible in any dielectric material. Two-dimensional (2D) materials have also garnered significant interest because of their unusual electromechanical properties and high flexibility, but the intrinsic flexoelectric properties of these materials remain unresolved. In this work, using atomistic modeling accounting for charge-dipole interactions, we report the intrinsic flexoelectric constants for a range of two-dimensional materials, including graphene allotropes, nitrides, graphene analogs of group-IV elements, and the transition metal dichalcogenides (TMDCs). We accomplish this through a proposed mechanical bending scheme that eliminates the piezoelectric contribution to the total polarization, which enables us to directly measure the flexoelectric constants. While flat 2D materials like graphene have low flexoelectric constants due to weak $\ensuremath{\pi}\ensuremath{-}\ensuremath{\sigma}$ interactions, buckling is found to increase the flexoelectric constants in monolayer group-IV elements. Finally, due to significantly enhanced charge transfer coupled with structural asymmetry due to bending, the TMDCs are found to have the largest flexoelectric constants, including ${\mathrm{MoS}}_{2}$ having a flexoelectric constant ten times larger than graphene.

Soft matter mechanics generally involve finite deformations and instabilities of structures in response to a wide range of mechanical and non-mechanical stimuli. Modeling plates and shells is generally a challenge due to their geometrically nonlinear response to loads; however, non-mechanical loads further complicate matters as it is often not clear how they modify the shell’s energy functional. In this work, we demonstrate how to form a mechanical interpretation of these non-mechanical stimuli, in which the standard shell strain energy can be augmented with potentials corresponding to how a non-mechanical stimulus acts to change the shell’s area and curvature via the natural stretch and curvature. As a result, the effect of non-mechanical stimuli to deform shells is transformed into effective external loadings, and this framework allows for the application of analytical and computational tools that are standard within the mechanics community. Furthermore, we generalize the effect of mass change during biological growth to account for its effect on the stress constitution. The theory is formulated based on a standard, stress-free reference configuration which is known a priori, meaning it can be physically observed, and only requires the solution of a single-field equation, the standard mechanical momentum or equilibrium equation, despite capturing the effects of non-mechanical stimuli. We validate the performance of this model by several benchmark problems, and finally, we apply it to complex examples, including the snapping of the Venus flytrap, leaf growth, and the buckling of electrically active polymer plates. Overall, we expect that mechanicians and non-mechanicians alike can use the approach presented here to quickly modify existing computational tools with effective external loadings calculated in this novel theory to study how various types of non-mechanical stimuli impact the mechanics and physics of thin shell structures.

Flexoelectricity is an electromechanical coupling occurring in dielectric materials that has recently attracted significant attention. The flexoelectric effect is described by a coupled, higher-order electromechanical set of equations that have typically been solved using a computationally expensive monolithic formulation. In the present work, we propose a staggered, explicit-implicit formulation that both significantly reduces the computational expense, while enabling the capturing of electromechanical instabilities through the usage of inertia. The higher order equations are discretized using an isogeometric formulation, and we demonstrate via two numerical examples the combination of increased computational efficiency with comparable accuracy that is gained from the proposed formulation as compared to the standard monolithic approaches.

The coefficient of thermal expansion, which measures the change in length, area, or volume of a material upon heating, is a fundamental parameter with great relevance for many applications. Although there are various routes to design materials with targeted coefficient of thermal expansion at the macroscale, no approaches exist to achieve a wide range of values in graphene-based structures. Here, we use molecular dynamics simulations to show that graphene origami structures obtained through pattern-based surface functionalization provide tunable coefficients of thermal expansion from large negative to large positive. We show that the mechanisms giving rise to this property are exclusive to graphene origami structures, emerging from a combination of surface functionalization, large out-of-plane thermal fluctuations, and the three-dimensional geometry of origami structures.

The thermal conductivity of two-dimensional materials like graphene can efficiently be tuned by introducing holes, in which the density and distribution of the holes are the key parameters. Furthermore, the distribution of holes can induce a variation as high as 74% in the thermal conductivity for porous graphene with a given density of holes. Therefore, an existing challenge is to find the optimal distribution of holes that can minimize or maximize the thermal conductivity of porous graphene as the design space expands dramatically with increasing hole density. We therefore apply an inverse design methodology based on machine learning to reveal the relationship between hole distribution and thermal conductivity reduction in monolayer graphene. The methodology reveals that holes that are randomly distributed transverse to the direction of heat flow, but that exhibit some periodicity along the direction of heat flow, represent the optimal distribution to minimizing the thermal conductivity for porous graphene. Lattice dynamics calculations and wave packet simulations reveal that this spatial distribution effectively causes localization of the phonon modes in porous graphene, which reduces the thermal conductivity. Overall, this work demonstrates the power of machine learning-based design approaches to efficiently obtain new physical insights for scientific problems of interest.

We present a novel structure for topologically protected propagation of mechanical waves in a continuous, elastic membrane using an analog of the quantum valley Hall effect. Our system involves a thin, continuous graphene monolayer lying on a pre-patterned substrate, and as such, it can be employed across multiple length scales ranging from the nano to macroscales. This enables it to support topologically-protected waves at frequencies that can be tuned from the kHz to GHz range by either selective pre-tensioning of the overlaying membrane, or by increasing the lattice parameter of the underlying substrate. We show through numerical simulations that this continuous system is robust against imperfections, is immune to backscattering losses, and supports topologically-protected wave propagation along all available paths and angles. We demonstrate the ability to support topologically-protected interface modes using monolayer graphene, which does not intrinsically support topologically non-trivial elastic waves.

Because the normal operation of the eye depends on sensitive morphogenetic processes for its eventual shape, developmental flaws can lead to wide-ranging ocular defects. However, the physical processes and mechanisms governing ocular morphogenesis are not well understood. Here, using analytical theory and nonlinear shell finite-element simulations, we show, for optic vesicles experiencing matrix-constrained growth, that elastic instabilities govern the optic cup morphogenesis. By capturing the stress amplification owing to mass increase during growth, we show that the morphogenesis is driven by two elastic instabilities analogous to the snap through in spherical shells, where the second instability is sensitive to the optic cup geometry. In particular, if the optic vesicle is too slender, it will buckle and break axisymmetry, thus, preventing normal development. Our results shed light on the morphogenetic mechanisms governing the formation of a functional biological system and the role of elastic instabilities in the shape selection of soft biological structures.

This year marks the eightieth anniversary of the invention of the finite element method (FEM). FEM has become the computational workhorse for engineering design analysis and scientific modeling of a wide range of physical processes, including material and structural mechanics, fluid flow and heat conduction, various biological processes for medical diagnosis and surgery planning, electromagnetics and semi-conductor circuit and chip design and analysis, additive manufacturing, i.e. virtually every conceivable problem that can be described by partial differential equations (PDEs). FEM has fundamentally revolutionized the way we do scientific modeling and engineering design, ranging from automobiles, aircraft, marine structures, bridges, highways, and high-rise buildings. Associated with the development of finite element methods has been the concurrent development of an engineering science discipline called computational mechanics, or computational science and engineering. In this paper, we present a historical perspective on the developments of finite element methods mainly focusing on its applications and related developments in solid and structural mechanics, with limited discussions to other fields in which it has made significant impact, such as fluid mechanics, heat transfer, and fluid-structure interaction. To have a complete storyline, we divide the development of the finite element method into four time periods: I. (1941-1965) Early years of FEM; II. (1966-1991) Golden age of FEM; III. (1992-2017) Large scale, industrial applications of FEM and development of material modeling, and IV (2018-) the state-of-the-art FEM technology for the current and future eras of FEM research. Note that this paper may not strictly follow the chronological order of FEM developments, because often time these developments were interwoven across different time periods.

Soft, active materials have been widely studied due to their ability to undergo large, complex shape changes in response to both mechanical and non-mechanical external stimuli. However, the vast majority of such studies has focused on investigating the forward problem, i.e. determining the shape changes that result from the applied stimuli. In contrast, very little work has been done to solve the inverse problem, i.e. that of identifying the external loads and stimuli that are needed to generate desired shapes and morphological changes. In this work, we present a new inverse methodology to study residual thermal expansion induced morphological changes in geometric composites made of soft, thin shells. In particular, the method presented in this work aims to determine the prescribed external stimuli needed to reconstruct a specific target shape, with a specific focus and interest in morphological changes from two-dimensional (2D) to three-dimensional (3D) shapes by considering the external stimuli within a thermohyperelastic framework. To do so, we utilize a geometrically exact, rotation-free Kirchhoff–Love shell formulation discretized by NURBS-based shape functions. We show that the proposed method is capable of identifying the stimuli, including cases where thermal expansion induced shape changes involving elastic softening occur in morphing from the initially flat 2D to non-planar 3D shapes. Validation indicates that the reconstructed shapes are in good agreement with the target shape.

We propose a computational methodology to perform inverse design of quantum spin hall effect (QSHE)-based phononic topological insulators. We first obtain two-fold degeneracy, or a Dirac cone, in the band structure using a level set-based topology optimization approach. Subsequently, four-fold degeneracy, or a double Dirac cone, is obtained by using zone folding, after which breaking of translational symmetry, which mimics the effect of strong spin-orbit coupling and which breaks the four-fold degeneracy resulting in a bandgap, is applied. We use the approach to perform inverse design of hexagonal unit cells of C6 and C3 symmetry. The numerical examples show that a topological domain wall with two variations of the designed metamaterials exhibit topologically protected interfacial wave propagation, and also demonstrate that larger topologically-protected bandgaps may be obtained with unit cells based on C3 symmetry.

Because phononic topological insulators have primarily been studied in discrete, graphenelike structures with ${C}_{6(v)}$ or ${C}_{3(v)}$ hexagonal symmetry, an open question is how to systematically achieve double Dirac cones and topologically nontrivial structures using continuous, nonhexagonal unit cells. Here, we address this challenge by presenting a computational methodology for the inverse design of continuous two-dimensional square phononic metamaterials exhibiting ${C}_{4(v)}$ and ${C}_{2(v)}$ symmetry. This leads to the systematic design of square unit cell topologies exhibiting a double Dirac degeneracy, which enables topologically protected interface propagation based on the quantum spin Hall effect (QSHE). Numerical simulations prove that helical edge states emerge at the interface between two topologically distinct square phononic metamaterials, which opens the possibility of QSHE-based pseudospin-dependent transport beyond hexagonal lattices.

International audience; We present a topology optimization framework to design periodic composites comprised of piezoelectric constituents that exhibit large flexoelectric constants. The novelty of the approach is that it leverages a representative volume element (RVE)-based computational homogenization approach that enables the analysis of periodic composites where the characteristic dimensions of the microstructure are significantly smaller than those of the structure, and as such requires only the optimization of a single RVE rather than that of the entire structure. We utilize this approach to analyze the enhancement in flexoelectric constants that can be achieved in different types of PZT-based composites, including hard-hard (PZT-PZT), and hard-soft (PZT-polymer composite, and porous PZT) structures. In all cases, significant enhancements are observed, with improvements between 2 and 15 times those of a naive guess, with some designs reaching a factor of one order of magnitude larger than BTO. We identify different mechanisms governing the enhanced electromechanical couplings, which can arise either from an enhancement of effective piezoelectricity in the RVE for PZT-PZT composites, or from a more subtle interplay involving the enhancement of effective piezoelectric and dielectric properties coupled with a reduction in mechanical compliance for PZT-polymer and porous PZT RVEs.

Machine learning (ML) methods have recently been used as forward solvers to predict the mechanical properties of composite materials. Here, we use a supervised-autoencoder (sAE) to perform inverse design of graphene kirigami, where predicting the ultimate stress or strain under tensile loading is known to be difficult due to nonlinear effects arising from the out-of-plane buckling. Unlike the standard autoencoder, our sAE is able not only to reconstruct cut configurations but also to predict mechanical properties of graphene kirigami and classify the kirigami witheither parallel or orthogonal cuts. By interpolating in the latent space of kirigami structures, the sAE is able to generate novel designs that mix parallel and orthogonal cuts, despite being trained independently on parallel or orthogonal cuts. Our method allows us to both identify novel designs and predict, with reasonable accuracy, their mechanical properties, which is crucial for expanding the search space for materials design.

In this work, we consider the stability of a spherical shell under combined loading from a uniform external pressure and a homogenous natural curvature. Non-mechanical stimuli, such as one that tends to modify the rest curvature of an elastic body, are prevalent in a wide range of natural and engineered systems, and may occur due to thermal expansion, changes in pH, differential swelling, and differential growth. Here, we investigate how the presence of both an evolving natural curvature and an external pressure modifies the stability of a complete spherical shell. We show that due to a mechanical analogy between pressure and curvature, positive natural curvatures can severely destabilize a thin shell, while negative natural curvatures can strengthen the shell against buckling, providing the possibility to design shells that buckle at or above the theoretical limit for pressure alone, i.e. a knock-up factor. These results extend directly from the classical analysis of the stability of shells under pressure, and highlight the important role that non-mechanical stimuli can have on modifying the the membrane state of stress in a thin shell.

The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.

While topological insulators have been widely studied, they typically appear in configurations and properties that are set once a structure is fabricated. As such, there is significant interest in developing topologically tunable or switchable concepts. In this work, we demonstrate that geometric nonlinearity in the form of an elastic snap-through instability can be exploited to switch the topological properties of a Timoshenko arch beam unit cell. We first demonstrate that the phonon band structure can be tuned using geometric nonlinearity and large displacement to reveal the existence of a topological phase transition point. To make this concept fully stable under the removal of the applied force, we then demonstrate the emergence of a bistable unit cell by varying the parameters of the unit cell. In doing so, we show that the bistability of the arch beam unit cell can be harnessed to design a switch that controls the topological nature of an interface between two different 1D phononic crystals.

Electroactive polymers such as dielectric elastomers (DEs) have attracted significant attention in recent years. Computational techniques to solve the coupled electromechanical system of equations for this class of materials have universally centered around fully coupled monolithic formulations, which while generating good accuracy requires significant computational expense. However, this has significantly hindered the ability to solve large scale, fully three-dimensional problems involving complex deformations and electromechanical instabilities of DEs. In this work, we provide theoretical basis for the effectiveness and accuracy of staggered explicit–implicit finite element formulations for this class of electromechanically coupled materials, and elicit the simplicity of the resulting staggered formulation. We demonstrate the stability and accuracy of the staggered approach by solving complex electromechanically coupled problems involving electroactive polymers, where we focus on problems involving electromechanical instabilities such as creasing, wrinkling, and bursting drops. In all examples, essentially identical results to the fully monolithic solution are obtained, showing the accuracy of the staggered approach at a significantly reduced computational cost.

Discrete stress relaxations (slip avalanches) in a model metallic glass under uniaxial compression are studied using a metadynamics algorithm for molecular simulation at experimental strain rates. The onset of yielding is observed at the first major stress drop, accompanied, upon analysis, by the formation of a single localized shear band region spanning the entire system. During the elastic response prior to yielding, low concentrations of shear transformation deformation events appear intermittently and spatially uncorrelated. During serrated flow following yielding, small stress drops occur interspersed between large drops. The simulation results point to a threshold value of stress dissipation as a characteristic feature separating major and minor avalanches consistent with mean-field modeling analysis and mechanical testing experiments. We further interpret this behavior to be a consequence of a nonlinear interplay of two prevailing mechanisms of amorphous plasticity, thermally activated atomic diffusion and stress-induced shear transformations, originally proposed by Spaepen and Argon, respectively. Probing the atomistic processes at widely separate strain rates gives insight to different modes of shear band formation: percolation of shear transformations versus crack-like propagation. Additionally a focus on crossover avalanche size has implications for nanomechanical modeling of spatially and temporally heterogeneous dynamics.

Understanding the time-dependent mechanical behavior of nanomaterials such as nanowires is essential to predict their reliability in nanomechanical devices. This understanding is typically obtained using creep tests, which are the most fundamental loading mechanism by which the time-dependent deformation of materials is characterized. However, due to existing challenges facing both experimentalists and theorists, the time-dependent mechanical response of nanowires is not well-understood. Here, we use atomistic simulations that can access experimental time scales to examine the creep of single-crystal face-centered cubic metal (Cu, Ag, Pt) nanowires. We report that both Cu and Ag nanowires show significantly increased ductility and superplasticity under low creep stresses, where the superplasticity is driven by a rate-dependent transition in defect nucleation from twinning to trailing partial dislocations at the micro- or millisecond time scale. The transition in the deformation mechanism also governs a corresponding transition in the stress-dependent creep time at the microsecond (Ag) and millisecond (Cu) time scales. Overall, this work demonstrates the necessity of accessing time scales that far exceed those seen in conventional atomistic modeling for accurate insights into the time-dependent mechanical behavior and properties of nanomaterials.

This article presents original work combining a NURBS-based inverse analysis with both kinematic and constitutive nonlinearities to recover the applied loads and deformations of thin shell structures. The inverse formulation is tackled by gradient-based optimization algorithms based on computed and measured displacements at a number of discrete locations. The proposed method allows accurately recovering the target shape of shell structures such that instabilities due to snapping and buckling are captured. The results obtained show good performance and applicability of the proposed algorithms to computer-aided manufacturing of shell structures.

The mechanical properties and plastic deformation mechanisms of metal nanowires have been studied intensely for many years. One of the important yet unresolved challenges in this field is to bridge the gap in properties and deformation mechanisms reported for slow strain rate experiments (∼10–2 s–1), and high strain rate molecular dynamics (MD) simulations (∼108 s–1) such that a complete understanding of strain rate effects on mechanical deformation and plasticity can be obtained. In this work, we use long time scale atomistic modeling based on potential energy surface exploration to elucidate the atomistic mechanisms governing a strain-rate-dependent incipient plasticity and yielding transition for face centered cubic (FCC) copper and silver nanowires. The transition occurs for both metals with both pristine and rough surfaces for all computationally accessible diameters (

Two-dimensional (2D) layered crystals are prone to bending and folding owing to their ultra-low bending stiffness. Folds are traditionally viewed as defects that degrade the material performance. Here, we demonstrate that folds and cohesive forces in 2D layered crystals like graphene and MoS2 can be exploited to collect and clean up interlayer impurities, wherein multiple separated impurities agglomerate into a single, large cluster. We combine classical molecular dynamics simulations and an analytical model to elucidate the competing roles of membrane bending and impurity-membrane cohesive energies in the self-cleaning process. Our findings shed light on the mechanisms by which the forces that are present in 2D layered crystals can positively impact, through the possibility of intrinsic cleaning and defect engineering, the synthesis of van der Waals homo- and heterostructures with improved reliability and functionalities.

We present a computational method that can be applied to capture surface stress and surface tension-driven effects in both stiff, crystalline nanostructures, like size-dependent mechanical properties, and soft solids, like elastocapillary effects. We show that the method is equivalent to the classical Young–Laplace model. The method is based on converting surface tension and surface elasticity on a zero-thickness surface to an initial stress and corresponding elastic properties on a finite thickness shell, where the consideration of geometric nonlinearity enables capturing the out-of-plane component of the surface tension that results for curved surfaces through evaluation of the surface stress in the deformed configuration. In doing so, we are able to use commercially available finite element technology, and thus do not require consideration and implementation of the classical Young–Laplace equation. Several examples are presented to demonstrate the capability of the methodology for modeling surface stress in both soft solids and crystalline nanostructures.

We demonstrate that large apparent converse flexoelectric properties can be obtained in piezoelectric composites using theoretical approaches. To do so, we first present a numerical homogenization method accounting for all electromechanical terms related to strain and the electric field gradient. We then evaluate the coefficients of the model by numerical simulations on periodic piezoelectric composites. After combining the homogenization approach with topology optimization to enhance the converse properties of the composite, we present numerical results that reveal that the apparent converse flexoelectric coefficients, as well as those associated with the higher order coupling terms involving the electric field gradient, are of the same order as the direct flexoelectric properties of the local constituents. These results suggest that both converse and higher order electromechanical coupling effects may contribute strongly to the flexoelectric response and properties of piezoelectric composites. Finally, we show that it is theoretically possible to obtain optimized designs of composites with apparent converse flexoelectric properties 1–2 orders of magnitude larger than ones obtained with naive guess designs.

We utilize atomistic simulations that account for point charges and dipoles to demonstrate that flexoelectricity, which arises from strain gradients, can be exploited to generate electricity from crumpled graphene sheets. Indentation of a circular graphene sheet generates localized developable (d)-cones, for which we verify the core radius and azimuthal angle with established theoretical models. We determine the voltage that can be generated based on the resulting electrostatic fields and compare the voltage generation to previous theoretical predictions that are scaled down to the nanoscale. In doing so, we find that the voltage generated from crumpling graphene exceeds, by about an order of magnitude, the expected voltage generation, indicating the benefit of exploiting the large strain gradients that are possible at the nanoscale. Finally, we demonstrate that crumpling may be a superior mechanism of flexoelectric energy generation as compared to bending of two-dimensional nanomaterials.

Most materials expand upon heating because the coefficient of thermal expansion (CTE), the fundamental property of materials characterizing the mechanical response of the materials to heating, is positive. There have been some reports of materials that exhibit negative thermal expansion (NTE), but most of these have been in complex alloys, where NTE originates from the transverse vibrations of the materials. Here, we show using molecular dynamics simulations that some single crystal monatomic FCC metal nanowires can exhibit NTE along the length direction due to a novel thermomechanical coupling. We develop an analytic model for the CTE in nanowires that is a function of the surface stress, elastic modulus, and nanowire size. The model suggests that the CTE of nanowires can be reduced due to elastic softening of the materials and also due to surface stress. For the nanowires, the model predicts that the CTE reduction can lead to NTE if the nanowire Young's modulus is sufficiently reduced while the nanowire surface stress remains sufficiently large, which is in excellent agreement with the molecular dynamics simulation results. Overall, we find a "smaller is smaller" trend for the CTE of nanowires, leading to this unexpected, surface-stress-driven mechanism for NTE in nanoscale materials.

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered. Among the many unique and attractive properties of 2D materials, mechanical properties play important roles in manufacturing, integration and performance for their potential applications. Mechanics is indispensable in the study of mechanical properties, both experimentally and theoretically. The coupling between the mechanical and other physical properties (thermal, electronic, optical) is also of great interest in exploring novel applications, where mechanics has to be combined with condensed matter physics to establish a scalable theoretical framework. Moreover, mechanical interactions between 2D materials and various substrate materials are essential for integrated device applications of 2D materials, for which the mechanics of interfaces (adhesion and friction) has to be developed for the 2D materials. Here we review recent theoretical and experimental works related to mechanics and mechanical properties of 2D materials. While graphene is the most studied 2D material to date, we expect continual growth of interest in the mechanics of other 2D materials beyond graphene.

Nanomaterials have recently been found to exhibit auxetic behavior, or a negative Poisson’s ratio, whereby the lateral dimensions of the material expand, rather than shrink, in response to applied tensile loading. In this brief review, we use the form of question–answer to highlight key points and outstanding issues related to the field of auxetic nanomaterials.

This paper presents a design methodology based on a combination of isogeometric analysis (IGA), level set and point wise density mapping techniques for topology optimization of piezoelectric/flexoelectric materials. The fourth order partial differential equations (PDEs) of flexoelectricity, which require at least C 1 continuous approximations, are discretized using Non-Uniform Rational B-spline (NURBS). The point wise density mapping technique with consistent derivatives is directly used in the weak form of the governing equations. The boundary of the design domain is implicitly represented by a level set function. The accuracy of the IGA model is confirmed through numerical examples including a cantilever beam under a point load and a truncated pyramid under compression with different electrical boundary conditions. Finally, we provide numerical examples demonstrating the significant enhancement in electromechanical coupling coefficient that can be obtained using topology optimization.

We investigate the mechanical properties of copper (Cu) octet-truss nanolattices through a combination of classical molecular dynamics (MD) simulations and theoretical analysis. The MD simulations show that Cu nanolattices with high relative density are stronger than bulk Cu, while also achieving higher strength at a lower relative density as compared to Cu meso-lattices. We demonstrate that modifying the classical octet-truss lattice model by accounting for nodal volume and bending effects through the free body diagram method is critical to obtaining good agreement between the theoretical model and the MD simulations. In particular, we find that as the relative density increases, nodal volume is the key factor governing the stiffness scaling of the nanolattices, while bending dominates the strength scaling. Most surprisingly, our analytic modeling shows that surface effects have little influence on the stiffness and strength scaling of the nanolattices, even though the cross sectional sizes of the nanowires that act as the lattice struts are on the order of 6 nm or smaller. This is because, unlike for individual nanowires, the mechanical response of the nanowire struts that form the nanolattice structure is also a function of bending and nodal volume effects, all of which depend nonlinearly on the nanolattice relative density. Overall, these results imply that nanoscale architected materials can access a new regime of architected material performance by simultaneously achieving ultrahigh strength and low density.

We perform molecular dynamics simulations to investigate the Poisson's ratio of graphene oxide. We find that the Poisson's ratio can be effectively tuned by increasing the degree of oxidation of graphene oxide. More specifically, the Poisson's ratio decreases linearly from positive to negative with increasing oxidation, turning negative at room temperature for a degree of oxidation of 0.27, and reaching a value of -0.567 for fully oxidized graphene. The oxidation dependence of the Poisson's ratio is attributed to the tension-induced suppression of the ripples resulting from the oxidation, whose amplitude increases with increasing oxidation. Finally, we also demonstrate the temperature dependence of the Poisson's ratio in the graphene oxide.

We characterize the force-dependent unfolding pathways and intermediate configurations of the green fluorescence protein (GFP) using novel atomistic simulations based on potential energy surface exploration. By using this approach, we are able to unfold GFP to significantly longer end-to-end distances, i.e. 40 nm, as compared to that seen in previous atomistic simulation studies. We find that there are four intermediate states between 5 and 40 nm end-to-end distance, where the intermediate configurations and unfolding pathways are strongly force-dependent. We additionally calculate the force-dependent lifetime of the 14 nm α β 1 intermediate, and demonstrate that it obeys Bell’s formula.

We present an extended finite element formulation for piezoelectric nanobeams and nanoplates that is coupled with topology optimization to study the energy harvesting potential of piezoelectric nanostructures. The finite element model for the nanoplates is based on the Kirchoff plate model, with a linear through the thickness distribution of electric potential. Based on the topology optimization, the largest enhancements in energy harvesting are found for closed circuit boundary conditions, though significant gains are also found for open circuit boundary conditions. Most interestingly, our results demonstrate the competition between surface elasticity, which reduces the energy conversion efficiency, and surface piezoelectricity, which enhances the energy conversion efficiency, in governing the energy harvesting potential of piezoelectric nanostructures.

We present a computational design methodology for topology optimization of multi-material-based flexoelectric composites. The methodology extends our recently proposed design methodology for a single flexoelectric material. We adopt the multi-phase vector level set (LS) model which easily copes with various numbers of phases, efficiently satisfies multiple constraints and intrinsically avoids overlap or vacuum among different phases. We extend the point wise density mapping technique for multi-material design and use the B-spline elements to discretize the partial differential equations (PDEs) of flexoelectricity. The dependence of the objective function on the design variables is incorporated using the adjoint technique. The obtained design sensitivities are used in the Hamilton–Jacobi (H–J) equation to update the LS function. We provide numerical examples for two, three and four phase flexoelectric composites to demonstrate the flexibility of the model as well as the significant enhancement in electromechanical coupling coefficient that can be obtained using multi-material topology optimization for flexoelectric composites.

Making kirigami-inspired cuts into a sheet has been shown to be an effective way of designing stretchable materials with metamorphic properties where the 2D shape can transform into complex 3D shapes. However, finding the optimal solutions is not straightforward as the number of possible cutting patterns grows exponentially with system size. Here, we report on how machine learning (ML) can be used to approximate the target properties, such as yield stress and yield strain, as a function of cutting pattern. Our approach enables the rapid discovery of kirigami designs that yield extreme stretchability as verified by molecular dynamics (MD) simulations. We find that convolutional neural networks (CNN), commonly used for classification in vision tasks, can be applied for regression to achieve an accuracy close to the precision of the MD simulations. This approach can then be used to search for optimal designs that maximize elastic stretchability with only 1000 training samples in a large design space of $\sim 4\times10^6$ candidate designs. This example demonstrates the power and potential of ML in finding optimal kirigami designs at a fraction of iterations that would be required of a purely MD or experiment-based approach, where no prior knowledge of the governing physics is known or available.

In this paper, we introduce a new boundary control synthesis problem with temporal logic specifications for a wide range of linear partial differential equations. We leverage the finite element method (FEM) to reduce the problem to a control problem for discrete-time linear systems. The specifications are formalized using an extension of signal temporal logic (STL), called Spatial-STL (S-STL). A conservative procedure to reformulate the specification into a regular STL formula as part of the FEM reduction is presented. A mixed-integer linear encoding is then used to synthesize the control inputs from a given allowed set. We illustrate the algorithm by applying it to a heat propagation problem.