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37 results for “metamaterial”

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zenodo52/100

Zero Modes and Classification of Combinatorial Metamaterials

This dataset contains the simulation data of the combinatorial metamaterial as used for the paper 'Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials', as published in Physical Review Letters. In this paper, the data is used to classify each \(k \times k\) unit cell design into one of two classes (C or I) based on the scaling (linear or constant) of the number of zero modes \(M_k(n)\) for metamaterials consisting of an \(n\times n\) tiling of the corresponding unit cell. Additionally, a random walk through the design space starting from class C unit cells was performed to characterize the boundary between class C and I in design space. A more detailed description of the contents of the dataset follows below. Modescaling_raw_data.zip This file contains uniformly sampled unit cell designs for metamaterial M2 and \(M_k(n)\) for \(1\leq n\leq 4\), which was used to classify the unit cell designs for the data set. There is a small subset of designs for \(k=\{3, 4, 5\}\) that do not neatly fall into the class C and I classification, and instead require additional simulation for \(4 \leq n \leq 6\) before either saturating to a constant number of zero modes (class I) or linearly increasing (class C). This file contains the simulation data of size \(3 \leq k \leq 8\) unit cells. The data is organized as follows. Simulation data for \(3 \leq k \leq 5\) and \(1 \leq n \leq 4\) is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4.npy", and contain a [Nsim, 1+k*k+4] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows: <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form. </li> <li>col k*k+1 - k*k+5: number of zero modes \(M_k(n)\) in ascending order of \(n\), so: \(\{M_k(1), M_k(2), M_k(3), M_k(4)\}\).</li> </ul> Note: the unit cell design uses the numbers \(\{0, 1, 2, 3\}\) to refer to each building block orientation. The building block orientations can be characterized through the orientation of the missing diagonal bar (see Fig. 2 in the paper), which can be Left Up (LU), Left Down (LD), Right Up (RU), or Right Down (RD). The numbers correspond to the building block orientation \(\{0, 1, 2, 3\} = \{\mathrm{LU, RU, RD, LD}\}\). Simulation data for \(3 \leq k \leq 5\) and \(1 \leq n \leq 6\) for unit cells that cannot be classified as class C or I for \(1 \leq n \leq 4\) is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4_classX_extend.npy", and contain a [Nsim, 1+k*k+6] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows: <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form. </li> <li>col k*k+1 - k*k+5: number of zero modes \(M_k(n)\) in ascending order of \(n\), so: \(\{M_k(1), M_k(2), M_k(3), M_k(4), M_k(5), M_k(6)\}\).</li> </ul> Simulation data for \(6 \leq k \leq 8\)  unit cells are stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. Note that the number of modes is now calculated for \(n_x \times n_y\) metamaterials, where we calculate \((n_x, n_y) = \{(1,1), (2, 2), (3, 2), (4,2), (2, 3), (2, 4)\}\) rather than \(n_x=n_y=n\) to save computation time. These files are named "data_new_rrQR_i_n_Mx_My_n4_kxk(_extended).npy", and contain a [Nsim, 1+k*k+8] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows: <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form. </li> <li>col k*k+1 - k*k+9: number of zero modes \(M_k(n_x, n_y)\) in order: \(\{M_k(1, 1), M_k(2, 2), M_k(3, 2), M_k(4, 2), M_k(1, 1), M_k(2, 2), M_k(2, 3), M_k(2, 4)\}\).</li> </ul> Simulation data of metamaterial M1 for \(k_x \times k_y\) metamaterials are stored in compressed numpy array format (.npz) and can be loaded in Python with the Numpy package using the numpy.load command. These files are named "smiley_cube_x_y_\(k_x\)x\(k_y\).npz", which contain all possible metamaterial designs, and "smiley_cube_uniform_sample_x_y_\(k_x\)x\(k_y\).npz", which contain uniformly sampled metamaterial designs. The configurations are accessed with the keyword argument 'configs'. The classification is accessed with the keyword argument 'compatible'. The configurations array is of shape [Nsim, \(k_x\), \(k_y\)], the classification array is of shape [Nsim]. The building blocks in the configuration are denoted by 0 or 1, which correspond to the red/green and white/dashed building blocks respectively. Classification is 0 or 1, which corresponds to I and C respectively. Modescaling_classification_results.zip This file contains the classification, slope, and offset of the scaling of the number of zero modes \(M_k(n)\) for the unit cells of metamaterial M2 in Modescaling_raw_data.zip. The data is organized as follows. The results for \(3 \leq k \leq 5\) based on the \(1 \leq n \leq 4\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows: col 0: label number to keep track col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n \leq 4\)) col 2: slope from \(n \geq 2\) onward (undefined for class X) col 3: the offset is defined as \(M_k(2) - 2 \cdot \mathrm{slope}\) col 4: \(M_k(1)\) The results for \(3 \leq k \leq 5\) based on the extended \(1 \leq n \leq 6\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4_classC_extend.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows: col 0: label number to keep track col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n \leq 6\)) col 2: slope from \(n \geq 2\) onward (undefined for class X) col 3: the offset is defined as \(M_k(2) - 2 \cdot \mathrm{slope}\) col 4: \(M_k(1)\) The results for \(6 \leq k \leq 8\) based on the \(1 \leq n \leq 4\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scenx_Sceny_slopex_slopey_offsetx_offsety_M1k_kxk(_extended).txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows: col 0: label number to keep track col 1: the class_x based on \(M_k(n_x, 2)\), where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n_x \leq 4\)) col 2: the class_y based on \(M_k(2, n_y)\), where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n_y \leq 4\)) col 3: slope_x from \(n_x \geq 2\) onward (undefined for class X) col 4: slope_y from \(n_y \geq 2\) onward (undefined for class X) col 5: the offset_x is defined as \(M_k(2, 2) - 2 \cdot \mathrm{slope_x}\) col 6: the offset_x is defined as \(M_k(2, 2) - 2 \cdot \mathrm{slope_y}\) col 7: \(M_k(1, 1)\) Additionally, results including classification for M2.ii can be found in the "results_analysis_unimodal_vs_oligomodal_vs_plurimodal_i_Scen_slope_M_M1k_kxk.txt and "results_analysis_unimodal_vs_oligomodal_vs_plurimodal_i_Scenx_Sceny_slopex_slopey_Mx_My_M1k_kxk.txt" files. Random Walks Data This file contains the random walks for \(3 \leq k \leq 8\) unit cells of metamaterial M2. The random walk starts from a class C unit cell design (classification M2.ii), for each step \(s\) a randomly picked unit cell is changed to a random new orientation for a total of \(s=k^2\) steps. The data is organized as follows. The configurations for each step are stored in the files named "configlist_test_i.npy", where i is a number and corresponds to a different starting unit cell. The stored array has the shape [k*k+1, 2*k+2, 2*k+2]. The first dimension denotes the step \(s\), where \(s=0\) is the initial configuration. The second and third dimension denote the unit cell configuration in the pixel representation (see paper) padded with a single pixel wide layer using periodic boundary conditions.  The class for each configuration are stored in "lmlist_test_i.npy", where i corresponds to the same number as for the configurations in the "configlist_test_i.npy" file. The stored array has the shape [k*k+1], where the index corresponds to the step \(s\) and displays the class for the accompanying unit cell. The stored number corresponds to the class as \(\{0, 1\} = \{\mathrm{I}, \mathrm{C}\}\).

opencc-by-4.0Feb 2022View details →

zenodo48/100

Convolutional Neural Networks for Classifying Combinatorial Metamaterials

This dataset contains the training and test data, as well as the trained neural networks as used for the paper 'Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials', as published in Physical Review Letters. In this paper, a neural network is used to classify each \(k \times k\) unit cell design of metamaterial M1 and M2 into one of two classes (C or I). Additionally, the performance of the trained networks is analysed in detail. A more detailed description of the contents of the dataset follows below. NeuralNetwork_train_and_test_data.zip This file contains the train and test data used to train the Convolutional Neural Networks (CNNs) of the paper. Each unit cell size has its own file, and is saved in a zipped numpy file type (.npz). It contains data for metamaterial M1 ("smiley_cube"), and metamaterial M2 classification (i) ("prek_xy") and (ii) ("unimodal_vs_oligomodal_inc_stripmodes"). CNN_saves_kxk.zip This file contains the parameter configurations of the CNNs trained on \(k \times k\) unit cells for metamaterial M2 classification (ii). Classification (i) is denoted by an additional M2ii in the file name. Metamaterial M1 is denoted by an extra M1 in the file name. Every hyperparameter (number of filters nf, number of hidden neurons nh, learning rate lr) combination is saved separately. The neural networks can be loaded using Google's TensorFlow package in Python, specifically using the 'tf.keras.models.load_model' function.

opencc-by-4.0Feb 2022View details →

zenodo44/100

Computational Design of Multimodal Combinatorial Mechanical Metamaterials

This dataset contains the data used to design multimodal mechanical metamaterials as described in the paper 'Prospecting for Pluripotency in Metamaterial Design', as published in Phys. Rev. Research 7(2), 023299. In this paper, the data is used to design 5×5 unit cells with desired deformation (zero) modes. The dataset contains the data used to train neural networks (CNN_data.zip), the designs generated by genetic algorithm (step_i.zip) and their mode structures (step_ii.zip), and the designs obtained through our design approach as described in the paper (step_ii.zip). Additionally, there is data comparing the efficiency of using a genetic algorithm or a hill climbing method to generate designs with a large number of intensive modes (step_i.zip).

opencc-by-4.0Jun 2024View details →

zenodo44/100

Supplementary codes and datasets for "Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials"

This repository provides data and codes for manuscript “Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials”  by M. Do&scaron;kář, M. Somr, R. Hlůžek, J. Havelka, J. Novák, and J. Zeman, published first as a preprint <a href="https://arxiv.org/abs/2305.09280">arXiv:2305.09280</a> at arXiv.org; see the actual description of the Zenodo entry for the latest reference. This repository contains: <ol> <li>MATLAB and C++ source codes for combinatorial design and numerical analyses (folder <code>./numerics/</code>),</li> <li>experimental data (folder <code>./experiments/</code>),</li> <li>3D models of parts used in robotic-assembly (folder <code>./models/</code>),</li> <li>a control script for robotic assembly (folder <code>./robotics/</code>).</li> </ol> Numerics All simulations were performed with an in-house MATLAB code, which extends the finite element toolbox for finite strain calculations accompanying the work of <a href="https://doi.org/10.1016/j.cma.2020.113333">van Bree, S. E. H. M., Roko&scaron;, O., Peerlings, R. H. J., Do&scaron;kář, M., & Geers, M. G. D. (2020). A Newton solver for micromorphic computational homogenization enabling multiscale buckling analysis of pattern-transforming metamaterials. Computer Methods in Applied Mechanics and Engineering, 372, 113333</a>. In particular, this snapshot corresponds to a cleaned-up version (excluding files unrelated to the publications) of commit <code>21cfc2e9</code>. The MATLAB codebase contains MEX files written in C++ to accelerate selected procedures. In order to run any code, these MEX files must be compiled first. We use CMake build automation, with the main <code>CMakeLists.txt</code> located in <code>./numerics/mex</code>. Combinatorial search was performed by the <code>RUN_modular_exploration.m</code> script; see definition of problems with the script. The results of the enumerations, stored in <code>./dat/exploration</code>, were analysed with <code>POST_modular_S_v3.m</code>, identifying layouts leading to the extreme (min/max) tilt angles. Comparison against experimental measurements was facilitated by a series of scripts <code>POST_DIC_{...}.m</code>. First, run <code>POST_DIC_step1_extract_points_in_mesh.m</code> to identify locations.mat. Next, post-process extensometer data with <code>POST_DIC_step2_merge_extensometer_data.m</code>, and use <code>POST_DIC_step3_impose_extracted_BC.m</code> to parse DIC results in a format suitable for imposing BC later in this script. Finally, comparison between experimental and computed displacements is provided by <code>POST_DIC_step4_modular_comparison_experiments.m</code>. (Note that the particular files need to be manually provided in the “Compute deformation process” part of <code>POST_DIC_step4_modular_comparison_experiments.m</code>.) Experimental data This folder contains data from (i) an unixaial tension test of a dogbone specimen (both from a MTS loading machine and DIC data) and (ii) two measurement sessions extracting the L-shape domain responses using DIC (<code>20_11_30 - Hluzek_Elka_newassemblyplan</code> and <code>21_04_12 - Hluzek_ Elka_quarters</code> with lower loading threshold). For post-processing, see the above-mentioned <code>POST_DIC_{...}.m</code> scripts. <code>*.mat</code> files present directly in <code>./experiments/</code> folder were obtained and are need by those scripts. 3D models The folder contains geometrical models for individual parts needed for robot-assisted assembly of module molds for casting. This includes: <ol> <li>a silo extension to store more tiles (file <code>silo_extension.stl</code>),</li> <li>formwork modules around the main structure for the purpose of casting silicone (file <code>tile_formwork.stl</code>),</li> <li>all types of tiles for the inside structure (file <code>tile_inside_types.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_1.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_2.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_3.stl</code>),</li> <li>connection for spacers (file <code>yumi_base_4.stl</code>),</li> <li>spacer holding a silo and the build plate (file <code>yumi_base_5.stl</code>),</li> <li>YuMi grippers with extensions to hold the tiles (file <code>grippers_extend.st</code>).</li> </ol> Robotics The folder contains a single file with a script created in RobotStudio (RobotWare Version: 6.08.01.00, SmartGripper Version: 3.55.0000.00) to assemble the plan with YuMi IRB 14000-0.5/0.5 left hand. Acknowledgement The related research, experiments, and code development were supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.

opencc-by-4.0May 2023View details →

zenodo40/100

The Extreme Mechanics of Viscoelastic Metamaterials

This dataset contains all data as used for the paper 'The Extreme Mechanics of Viscoelastic Metamaterials'. It provides example files on viscoelastic modelling in Abaqus and on deriving viscoelastic material properties,   Abstract: Mechanical metamaterials made of flexible building blocks can exhibit a plethora of extreme mechanical responses, such as negative elastic constants, shape-changes, programmability and memory. To date, dissipation has largely remained overlooked for such flexible metamaterials. As a matter of fact, extensive care has often been devoted in the constitutive materials’ choice to avoid strong dissipative effects. However, in an increasing number of scenarios, where metamaterials are loaded dynamically, dissipation can not be ignored. In this review, we show that the interplay between mechanical instabilities and viscoelasticity can be crucial and can be harnessed to obtain new functionalities. We first show that this interplay is key to understanding the dynamical behaviour of flexible dissipative metamaterials that use buckling and snapping as functional mechanisms. We further discuss the new opportunities that spatial patterning of viscoelastic properties offer for the design of mechanical metamaterials with properties that depend on loading rate.

opencc-by-4.0Mar 2022View details →

zenodo40/100

Data for Metamaterial Eigenmodes beyond Homogenization

Raw data for figures in https://arxiv.org/abs/2201.09559 mostly generated by https://doi.org/10.5281/zenodo.6557265

opencc-by-4.0May 2022View details →

zenodo40/100

Origami Metamaterials: Design, Symmetries, and Combinatorics

Raw data files + python processing and plotting scripts of the experiments shown in chapter 5 of thesis: Origami Metamaterials: Design, Symmetries, and Combinatorics Author: P. Dieleman Supervisor: prof. M. van Hecke affiliation: Leiden Institute of Physics ISBN: 978-90-8593-361-8 under embargo until: 16-04-2020

opencc-by-4.0Dec 2017View details →

zenodo40/100

Determining the Complex Jones Matrix Elements of a Chiral 3D Optical Metamaterial

Research Data supporting “Determining the Complex Jones Matrix Elements of a Chiral 3D Optical Metamaterial” original research article published in: APL Photonics 2019, 4 (12), <a href="http://dx.doi.org/10.1063/1.5127169">http://dx.doi.org/10.1063/1.5127169</a> The data of the dataset is arranged into different folders (.zip file), containing the following files (.txt, .tif files; italics). This data and the descriptions below should be read in conjunction with the manuscript and “Supporting Info”, both of which may be found at the following DOI: <a href="http://dx.doi.org/10.1063/1.5127169">http://dx.doi.org/10.1063/1.5127169</a>

opencc-by-4.0Dec 2018View details →

zenodo40/100

Automated discovery of reprogrammable nonlinear dynamic metamaterials — Data

This dataset includes optimization and experimental data complementing the paper: <a href="https://doi.org/10.1038/s41563-024-02008-6" target="_blank" rel="noopener">G. Bordiga, E. Medina, S. Jafarzadeh, C. Boesch, R. P. Adams, V. Tournat, K. Bertoldi. Automated discovery of reprogrammable nonlinear dynamic metamaterials. Nature Materials. (2024)</a>. Optimization and post-processing data in this dataset were generated using the code <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a> developed for the paper. This dataset can be loaded and visualized using <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a> with the following steps: <ul> <li>Install <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a>.</li> <li>Download <code>data.zip</code> from this dataset.</li> <li>Extract <code>data.zip</code> and place its content in a <code>data</code> folder in the root of <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a>.</li> <li>Load the data associated with the design problems shown in the paper using the <a href="https://github.com/bertoldi-collab/DifFlexMM/tree/main/notebooks" target="_blank" rel="noopener">notebooks</a>.</li> </ul> For more information on each problem, please refer to the <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">README</a>. Videos illustrating the solved design problems can be viewed at <a href="https://github.com/bertoldi-collab/DifFlexMM/tree/main/videos" target="_blank" rel="noopener">DifFlexMM/videos</a>.

opencc-by-4.0Jul 2024View details →

zenodo40/100

Supplementary data for study on "Superplastic 3D printed nitinol woven metamaterials lead to dramatic variations of mechanical properties by design"

Raw and processed data from experimental compression testing of 3D printed nitinol lattices and wovens are provided as supplementary materials for the mentioned study, submitted for evaluation to the journal of Virtual and Physical Prototyping.

opencc-by-4.0Oct 2024View details →

zenodo40/100

Tunable photo-responsive elastic metamaterials

Supplementary information for Gliozzi, A.S., Miniaci, M., Chiappone, A. et al. Tunable photo-responsive elastic metamaterials. Nat Commun 11, 2576 (2020). https://doi.org/10.1038/s41467-020-16272-y and raw data for Fig. 3 therein. Each file contains data for the spectra corresponding to each different temperature. The file has a suffix XXC, where XX indicates the temperature of the climate chamber in decrees C. Data are organized in two columns: the first is frequency in kHz, the second is FFT magnitude in dB The Video file 'represents the evolution of the transmission spectrum under illumination (data reltive to Fig. 2c in the main text), with 1 spectrum per second. At t=100s the sample is illuminated (first pillar) until t=360s. Then the spectrum reverts to the initial one

opencc-by-4.0May 2020View details →

dryad40/100

Data from: Characterizing the hyperuniformity of disordered network metamaterials

Open the record for dataset details and reuse information.

publicDec 2025View details →

zenodo36/100

Dataset for Hyperbolic Optical Metamaterials from Shear-Aligned Block Copolymer Cylinder Arrays

Research Data supporting “Hyperbolic Optical Metamaterials from Shear-Aligned Block Copolymer Cylinder Arrays” Published in Advanced Photonics Research doi: 10.1002/adpr.202000037

opencc-by-4.0Oct 2020View details →

zenodo36/100

Selective dynamic band gap tuning in metamaterials using graded photoresponsive resonator arrays

Raw Data for figures: Fig. 2: Dispersion diagrams for non-illuminated (off) and illuminated (on) pillars of different heights (hp). hp1 = 7 mm, hp2 = 9 mm, hp3 = 11 mm, hp4 = 13 mm; p = 0 (1) for purely in- (out-of-plane) behavior Fig. 4: Computed transmission spectrum of a finite structure. a) Numerically simulated transmission spectrum for the considered 8-pillar specimen, both without ("Laser off") and with laser illumination ("Laser on 7th pillar"). Fig. 5: Transmission spectrum of the finite structure considered experimentally. a) Measured spectra before (blue) and after (red) illumination of pillar 1. Band gaps are highlighted in light blue and numbered from I to IV; b) Corresponding colour map representing transmission vs. frequency and time (vertical axis) when switching laser illumination on (t = 700 s) and off (t = 2300 s); c) same as a), with illumination of pillar 6; d) same as b), with illumination on pillar 6. Fig. 6: Dynamic modulation of signal frequencies (f1 = 21.5 kHz, f2 = 71.5 kHz) in a graded pillar structure. The different temporal intervals depict tunable suppression and enhancement of specific frequencies through selective pillar illumination.

opencc-by-4.0Mar 2024View details →

dryad36/100

DNA-silica nanolattices as mechanical metamaterials

Mechanical metamaterials consist of intricate periodic structures made using additive manufacturing techniques. Yet, nanoscale (<10 nm) features cannot be directly printed using these fabrication techniques, although this is the size regime in which enhanced material properties appear. In addition, current additive manufacturing techniques cannot easily combine disparate materials (e.g., soft, biological polymers with hard ceramics) into structural motifs. Here, we show that DNA origami can be used to construct octahedral-based isotropic and anisotropic nanolattices. When coated with a thin silica layer, these nanolattices obtain strength and energy absorption competitive with the best additively manufactured nanolattices. The DNA nanolattices have strut 6 nm diameters and ~50 nm unit cells, which are two orders of magnitude smaller than lithography-based lattices. The silica coating is as thin as ~1.65 nm, which results in enhanced strength. Atom probe tomography confirms the nanoscale distribution of DNA and silica in the octahedral lattice geometry. Finite element modeling (FEM) reveals two dominant failure modes: buckling at lower coating thicknesses and tensile fracture at higher thicknesses. Molecular dynamics (MD) simulations reveal that the DNA delays failure by suppressing buckling within the lattice struts, while the nanoscale silica undergoes a surface buckling mode which contributes to increased strength at large strains.

opencc-zeroMar 2024View details →

zenodo36/100

Mechanical Metamaterial: Square Array of Circular Holes Under Deformation

This repository contains data for a research project involving graph neural networks (GNNs) applied to mechanical metamaterials and their deformations.  The mechanical metamaterials of interest consist of a flexible rubber-like material with a square grid of almost-circular holes in it, of various diameters. The holes are not quite circular, because they were made slightly elliptic to avoid the bifurcation point. This data was used to obtain the results in the paper 'Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials'. The data includes PyTorch Geometric Graph objects, raw data from MATLAB simulations, and files describing the finite element mesh. The Jupyter notebook 'data_create_graphs.ipynb' in the GitHub repository can be used to turn the raw data and mesh information into the graph objects. GitHub repository with the code: <a title="https://github.com/FHendriks11/SimEGNN" href="https://github.com/FHendriks11/SimEGNN">https://github.com/FHendriks11/SimEGNN</a> Link to the corresponding paper Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials: <a href="https://www.sciencedirect.com/science/article/pii/S0045782525001392">https://www.sciencedirect.com/science/article/pii/S0045782525001392</a> (also on ArXiv: <a title="https://arxiv.org/abs/2404.17365" href="https://arxiv.org/abs/2404.17365">https://arxiv.org/abs/2404.17365</a>) The .pkl files are pickle files and can be opened in Python using the standard pickle library. The mesh files, which have the extension .mat, are MatLab files and can be opened either in MatLab or in Python using scipy.io.loadmat from the scipy library.

opencc-by-4.0Nov 2024View details →

zenodo36/100

Dataset and Code for the article "Metamaterial-enabled asymmetric negative refraction of GHz mechanical waves"

This submission contains scripts for generating ab-initio and/or from raw data the figures of the article "Metamaterial-enabled asymmetric negative refraction of GHz mechanical waves" by SIMONE ZANOTTO, GIORGIO BIASIOL, PAULO VENTURA SANTOS, AND ALESSANDRO PITANTI, published in NATURE COMMUNICATIONS, 2022.

opencc-by-4.0Sep 2022View details →

zenodo36/100

Dataset of Reconfigurable Metamaterial Processing Units that Solve Arbitrary Linear Calculus Equations

Dataset of Reconfigurable Metamaterial Processing Units that Solve Arbitrary Linear Calculus Equations

opencc-by-4.0Jun 2024View details →

zenodo36/100

Inverse Design of Metamaterials with Manufacturing-Aware Spectrum-to-Shape Diffusion Models

The dataset includes detailed information on the MIM tri-layer metamaterial structures designed and used for training the DiffMeta framework. Specifically, it contains 60000 data: Structural Data: Detailed geometric patterns and composition parameters of the designed MIM tri-layer metamaterial structures. Spectral Data: Spectral measurements on MIM tri-layer metamaterial structures, including emissivity, reflectivity and transmittance spectra across a range of wavelengths. The dataset is meticulously organized to facilitate the replication of our study and support further research in the field of metamaterial design.

opencc-by-4.0Jul 2024View details →

zenodo36/100

Attenuating surface gravity waves with mechanical metamaterials

Videos showing simulations of one or more submerged oscillators attenuating surface gravity waves, related to the publication  <a href="https://aip.scitation.org/author/de+Vita%2C+F">F. De Vita</a>, <a href="https://aip.scitation.org/author/de+Lillo%2C+F">F. De Lillo</a>, <a href="https://aip.scitation.org/author/Bosia%2C+F">F. Bosia</a>, and <a href="https://aip.scitation.org/author/Onorato%2C+M">M. Onorato</a>, "Attenuating surface gravity waves with mechanical metamaterials", Physics of Fluids 33, 047113 (2021) <a href="https://doi.org/10.1063/5.0048613">https://doi.org/10.1063/5.0048613</a> Also included are Data relative to Figs. 3, 5, 6, 8 and gnuplot scripts to generate the figures.

opencc-by-4.0Aug 2021View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record