Engineering lattice metamaterials for extreme property, programmability, and multifunctionality

Jia, Zian; Liu, Fan; Jiang, Xihang; Wang, Lifeng

doi:10.1063/5.0004724

Cited by 97 publications

(48 citation statements)

References 164 publications

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“…Designing 3D open lattices with complicated structural details and low coordination number presents a challenge because conventional non-patchy particles tend to pack closely, instead of allowing for periodic arrays of holes [107]. Open and highly ordered structures have tailorable properties, including light weight, high porosity, low thermal conductivity, tailored stress-strain response, and photonic bandgap [159], and benefiting applications in catalyst [160], photonics [149], and metamaterials [161]. One of the promising strategies to realize such open lattice in an energy efficient way is by hierarchical assembly of patchy building blocks, which has also been exploited in polyhedral DNA scaffolds [162], collagen fibrils [163], and microscale particles [164].…”

Section: Design Of Kinetic Pathways: To Achieve 3d Lattice Via Hierarmentioning

confidence: 99%

Patchy Nanoparticle Synthesis and Self-Assembly

Kim¹,

Yao²,

Kalutantirige³

et al. 2020

Self-Assembly of Nanostructures and Patchy Nanoparticles

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Biological building blocks (i.e., proteins) are encoded with the information of target structure into the chemical and morphological patches, guiding their assembly into the levels of functional structures that are crucial for living organisms. Learning from nature, researchers have been attracted to the artificial analogues, “patchy particles,” which have controlled geometries of patches that serve as directional bonding sites. However, unlike the abundant studies of micron-scale patchy particles, which demonstrated complex assembly structures and unique behaviors attributed to the patches, research on patchy nanoparticles (NPs) has remained challenging. In the present chapter, we discuss the recent understandings on patchy NP design and synthesis strategies, and physical principles of their assembly behaviors, which are the main factors to program patchy NP self-assembly into target structures that cannot be achieved by conventional non-patched NPs. We further summarize the self-assembly of patchy NPs under external fields, in simulation, and in kinetically controlled assembly pathways, to show the structural richness patchy NPs bring. The patchy NP assembly is novel by their structures as well as the multicomponent features, and thus exhibits unique optical, chemical, and mechanical properties, potentially aiding applications in catalysts, photonic crystals, and metamaterials as well as fundamental nanoscience.

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Section: Design Of Kinetic Pathways: To Achieve 3d Lattice Via Hierarmentioning

confidence: 99%

Patchy Nanoparticle Synthesis and Self-Assembly

Kim¹,

Yao²,

Kalutantirige³

et al. 2020

Self-Assembly of Nanostructures and Patchy Nanoparticles

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Section: Design Of Kinetic Pathways: To Achieve 3d Lattice Via Hierarchical Assemblymentioning

confidence: 99%

Self-Assembly of Nanostructures and Patchy Nanoparticles

Mehraeen¹

2020

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at Chicago. He received his M.Sc. and Ph.D. in Mechanical Engineering, both from Stanford University under the supervision of Andrew Spakowitz. For his Ph.D., he studied the impact of molecular elasticity on the behavior of semi-flexible polymers and protein self-assembly. As a postdoctoral scholar, he studied the impact of active layer morphology on bimolecular recombination losses in organic photovoltaics, and transition state theory under the supervision of Jean-Luc Bredas at Georgia Institute of Technology, and Jianshu Cao at MIT, respectively. He has published three books, more than 30 scientific papers, and served as a reviewer for major scientific journals. His current research focuses on applying molecular simulations, atomistic modeling, and density functional theory to address directed self-assembly of nanoparticles on templated surfaces, photochemistry of organic solar cells, and polymer and electrocatalyst design using machine learning and artificial intelligence.

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“…For instance, a parametric hyperelastic constitutive model for honeycomb-like and re-entrant 2D lattices was developed in Reference 17 and a nonlinear micropolar continuum model with an incremental representation of the constitutive behavior was proposed in Reference 18. In References 19,20, also generalized state-dependent continuum formulations were employed for multiscale modeling of 2D and 3D beam lattices subject to finite deformations, however, in a concurrent, FE 2 setting without an explicit representation of the effective constitutive relationship between macro-and microscales. Thus, for efficient multiscale simulation and optimization, compare Reference 21, it is of great importance to develop generic material models that can represent the constitutive behavior of nonlinear, anisotropic metamaterials and include their parametric dependencies.…”

Section: Introductionmentioning

confidence: 99%

Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials

Fernández

Fritzen

Weeger

2021

Numerical Meth Engineering

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Mechanical metamaterials such as open-and closed-cell lattice structures, foams, composites, and so forth can often be parametrized in terms of their microstructural properties, for example, relative densities, aspect ratios, material, shape, or topological parameters. To model the effective constitutive behavior and facilitate efficient multiscale simulation, design, and optimization of such parametric metamaterials in the finite deformation regime, a machine learning-based constitutive model is presented in this work. The approach is demonstrated in application to elastic beam lattices with cubic anisotropy, which exhibit highly nonlinear effective behaviors due to microstructural instabilities and topology variations. Based on microstructure simulations, the relevant material and topology parameters of selected cubic lattice cells are determined and training data with homogenized stress-deformation responses is generated for varying parameters. Then, a parametric, hyperelastic, anisotropic constitutive model is formulated as an artificial neural network, extending a recent work of the author extending a recent work of the author, Comput Mech., 2021;67(2):653-677. The machine learning model is calibrated with the simulation data of the parametric unit cell. The authors offer public access to the simulation data through the GitHub repository https://github.com/CPShub/ sim-data. For the calibration of the model, a dedicated sample weighting strategy is developed to equally consider compliant and stiff cells and deformation scenarios in the objective function. It is demonstrated that this machine learning model is able to represent and predict the effective constitutive behavior of parametric lattices well across several orders of magnitude. Furthermore, the usability of the approach is showcased by two examples for material and topology optimization of the parametric lattice cell.

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Engineering lattice metamaterials for extreme property, programmability, and multifunctionality

Cited by 97 publications

References 164 publications

Patchy Nanoparticle Synthesis and Self-Assembly

Patchy Nanoparticle Synthesis and Self-Assembly

Self-Assembly of Nanostructures and Patchy Nanoparticles

Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials

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