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25 results for “transition metal dichalcogenide”
Research data for "Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations"
<p>This dataset supports the paper "Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations". </p> <p><strong>Included Files:</strong></p> <ul> <li><strong>ocp_active.zip</strong>: Modified version of ocp (https://github.com/Open-Catalyst-Project/ocp) tailored for active learning applications.</li> <li><strong>deployed.pth</strong>: Pre-trained model used in the experiments.</li> <li><strong>chemiscopy_run.py</strong>: Script integrating the chemiscopy and nequip modules, designed for dataset visualization.</li> <li><strong>new_energy.py</strong>: Modified version of the nequip module, featuring a repulsive potential function.</li> <li><strong>test_datasets.extxyz</strong> & <strong>train_datasets.extxyz</strong>: The test and training datasets in extxyz format.</li> </ul> <p>How to use the modified version of the nequip module:</p> <p>To train this version of the potential function, it is recommended to use nequip<=0.5.6 (on Linux). The NequIP training files need to be updated as follows:</p> <pre><code>model_builders: - new_energy.EnergyModel - StressForceOutput min_bond_len: 1.8</code></pre> <p>Then run:</p> <p><code>export PYTHONPATH=${PYTHONPATH}:$PWD</code><br><code>nequip-train config.yml # Train the potential function</code><br><code>nequip-deploy build --train-dir nequipresultsdir build.pth # Deploy the trained model</code></p>
Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity
<p># Data and plotting code for "Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity" by Battulga Munkhbat, Piotr Wróbel, Tomasz J. Antosiewicz, and Timur O. Shegai, ACS Photonics (2022); https://doi.org/10.1021/acsphotonics.2c00433</p> <p><br> ## Contents</p> <p>* <TMD-material>: directories with raw and derived data for all 10 TMDs<br> * f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt: obtained permittivities<br> * plot_*_v1.m: Matlab scripts for plotting data</p> <p>## Description of the data</p> <p>The raw and derived data stored in directories <TMD> contain the following files:</p> <p>* <TMD>/<date>-<TMD>.SEsnap: binary data file with collected data, CompleteEASE format<br> * <TMD>/<date>-<TMD>-E*.mat: ascii text file with permittivity data separated into individual components as exported from CompleteEASE software<br> * <TMD>/<date>-<TMD>-full.mat: ascii text file with fitted model parameters as exported from CompleteEASE software<br> * <TMD>/<TMD>-data/*.txt: selected raw data and fits for all considered samples (Mueller Matrix or Delta/Psi/depolarization).</p> <p>The structure of the data file names is as follows:<br> <order-number-in-CompleteEASE>-s<sample-name>-<data-type>.txt for general ellipsometry (delta, psi, depolarization) or<br> <order-number-in-CompleteEASE>-s<sample-name>-o<in-plane-sample-rotation-number>-mm.txt for Mueller Matrix measurements.</p> <p>The following two scripts can be used to plot the raw measured data (solig lines) along with corresponding fits (black dotted lines):</p> <p>* plot_mm_v1.m: Matlab script for plotting Mueller Matrix data for WTe2 and ReS2<br> * plot_psi_delta_depol_v1.m: Matlab script for plotting psi, delta, and depolarization data for other TMDs</p> <p>The diagonal permittivity tensor data are saved in the f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt files which can be plotted using the plot_permittivity_v1.m Matlab script. The format of this file is as follows:</p> <p>wavelength in nanometers; real part of epsilon_xx; imaginary part of epsilon_xx; real part of epsilon_yy; imaginary part of epsilon_yy; real part of epsilon_zz; imaginary part of epsilon_zz;</p> <p> </p> <p> </p>
Data for First-Order Quantum Phase Transition in the Hybrid Metal-Mott Insulator Transition Metal Dichalcogenide 4Hb-TaS2
<p>Data to reproduce the main text figures of the paper "First-Order Quantum Phase Transition in the Hybrid<br> Metal-Mott Insulator Transition Metal Dichalcogenide 4Hb-TaS2" are provided. They are either in the *.txt format or *.fig (matlab figure) format. The *.txt files contain the relevant header information.</p>
Hydrogen Transport Between Layers of Transition Metal-Dichalcogenides
<p><strong>Abstract</strong></p><p>Hydrogen is a crucial source of green energy and has been extensively studied for its potential usage in fuel cells. The advent of two-dimensional crystals (2DCs) has taken hydrogen research to new heights, enabling it to tunnel through layers of 2DCs or be transported within voids between the layers, as demonstrated in recent experiments by Geim's group. In this study, we investigate how the composition and stacking of transition-metal dichalcogenide (TMDC) layers influence the transport and self-diffusion coefficients (D) of hydrogen atoms using well-tempered metadynamics simulations. Our findings show that modifying either the transition metal or the chalcogen atoms significantly affects the free energy barriers (∆F) and, consequently, the self-diffusion of hydrogen atoms between the 2DC layers. In the H^h_h polytype (2H stacking), MoSe2 exhibits the lowest ∆F, while WS2 has the highest, resulting in the largest D for the former system. Additionally, hydrogen atoms inside the R^M_h (or 3R) polytype encounter more than twice lower energy barriers and, thus, much higher diffusivity compared to those within the most stable H^h_h stacking. These findings are particularly significant when investigating twisted layers or homo- or heterostructures, as different stacking areas may dominate over others, potentially leading to directional transport and interesting materials for ion or atom sieving.</p><p>https://doi.org/10.48550/arXiv.2308.03418</p><p> </p><p><strong>Overview</strong></p><p>This repository contains calculation files, optimized structures, plots, and Excel files for studies of H diffusion within vdW crystals. A general guide is written here and also in the README.txt file. The repository classifies based on material type and each material has a specific README.txt file that contains more detailed information. Calculation towards self-diffusion coefficients for each step of each material follows:<br> </p><ol><li>Cell optimization of the pristine material: To optimize lattice constants and structural parameters.</li><li>Single H atom site tests. To find a favorable H location within vdW crystals to initialize dynamic calculations.</li><li>Molecular dynamics. Pre-optimization of thermal equilibrium before performing metadynamics calculations.</li><li>Well-tempered Metadynamics simulations: to get a free-energy surface and ultimately; to get a ∆F to calculate D.</li><li>The final FES is plotted, and the calculation of the self-diffusion coefficients is noted down in the Excel file.</li></ol><p> </p><p><strong>Acknowledgment</strong></p><p>This research was supported by the Deutsche Forschungsgemeinschaft (projects GRK 2721/1 and SFB 1415). The authors acknowledge the high-performance computing center of ZIH Dresden, the Leipzig University Computing Centre, and the Paderborn Center for Parallel Computing (PC2 ) for computational resources. The authors thank Prof. Thomas Heine for fruitful discussions.</p>
Dataset: Magic in transition metal dichalcogenide bilayers
<p>Dataset for the paper "Magic in transition metal dichalcogenide bilayers" (arXiv:2106.11954). </p>
Dataset for "Band structures and Z2 invariant of 2D transition metal dichalcogenides from fully relativistic Dirac--Kohn--Sham theory using Gaussian-type orbitals"
<p>Two-dimensional (2D) materials exhibit a wide range of remarkable phenomena, many of which owe their existence to the relativistic spin--orbit coupling (SOC). To understand and predict properties of materials containing heavy elements, such as the transition metal dichalcogenides (TMDs), full account of relativistic effects is mandatory in first-principles calculations. We present an all-electron method based on the four-component Dirac Hamiltonian and Gaussian-type orbitals (GTOs) that overcomes complications associated with linear dependencies and ill-conditioned matrices arising when diffuse functions are included in the basis. Until now, there has been no systematic study of the convergence of GTO basis sets for periodic solids neither at the nonrelativistic nor the relativistic level. Here, we provide such a study of relativistic band structures of the 2D TMDs in the hexagonal (2H), tetragonal (1T), and distorted tetragonal (1T') structural phases while focusing on SOC-driven properties (the Rashba splitting and the $\mathbb{Z}_2$ topological invariant). We demonstrate that our approach is valid even if large basis sets with multiple basis functions corresponding to each valence atomic orbital (denoted triple- and quadruple-$\zeta$) are used in the relativistic regime. The method does not require the use of pseudopotentials and provides access to all electronic states within the same framework, paving the way for direct studies of material properties that depend heavily on the electron density near atomic nuclei where relativistic effects and SOC are strongest, such as the parameters of the spin Hamiltonian.</p>
Atom-by-atom Imaging of Moiré Transformations in 2D Transition Metal Dichalcogenides
<p>This dataset contains the images used in the manuscript "Atom-by-atom Imaging of Moiré Transformations in 2D Transition Metal Dichalcogenides".</p>
Research data supporting 'Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1−xWxS2−2ySe2y Alloys'
<p>Research data supporting 'Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1−xWxS2−2ySe2y Alloys'</p>
Monte Carlo data for "Melting of generalized Wigner crystals in transition metal dichalcogenide heterobilayer Moiré systems"
<p>Monte Carlo lattice configuration snapshots used to produce the results in the paper "Melting of generalized Wigner crystals in transition metal dichalcogenide heterobilayer Moiré systems." For further details and analysis scripts, see the associated Github repository at https://github.com/KimGroup/tmd_moire_monte_carlo.</p>
Data and code for "Combining ultrahigh index with exceptional nonlinearity in resonant transition metal dichalcogenide nanodisks."
<div># Data and code for "Combining ultrahigh index with exceptional nonlinearity in resonant transition metal dichalcogenide nanodisks."</div> <div> </div> <div>## Contents</div> <div> </div> <div>* `chi2-dft`: DFT calculations of the chi(2) nonlinear susceptibility tensor</div> <div>* `shg-exp`: explicit spectra of the SHG from a flake and resonant disks</div> <div> </div> <div>## Description of the data</div> <div> </div> <div>The data are organized as follows:</div> <div> </div> <div>* `chi2-dft/plot_chi2.{py,m}`: plotting scripts for python and matlab</div> <div>* `chi2-dft/shg-3R-MoS2-chi2.*`: binary dataset for plotting chi2 and plot thereof</div> <div>* `chi2-dft/nlo`: folder with scripts for data reproduction and structure file</div> <div>* `chi2-dft/nlo/shg-lg`: folder with chi2 tensor elements, numpy format; used for plotting with python</div> <div> </div> <div>* `shg-exp/disk-specs`: data and matlab script to plot explicit SHG spectra of the disks</div> <div>`shg-exp/disk-specs/D*_***nm_1s_03mW.asc/` - D* - disk number 1,2,3; ***nm - pump wavelength 810-910-1020nm; 1s - signal collection time in seconds;03mW - pump power in mW</div> <div>* `shg-exp/flake-specs`: data and matlab script to plot explicit SHG spectra and log-log power dependence of the thin flake</div> <div>`shg-exp/flake-specs/***nm_*.*mw_0.5s_H8_3R_MoS2.asc/` - ***nm - pump wavelength 800:10:1040nm; *.*mW - pump power in mW; 0.5s - signal collection time in seconds;</div> <div> </div> <div>## Reproduction of the data</div> <div> </div> <div>The DFT calculations were performed using GPAW (https://wiki.fysik.dtu.dk/gpaw/index.html) and the nonlinear optical response approach (https://wiki.fysik.dtu.dk/gpaw/tutorialsexercises/opticalresponse/nlopt/nlopt.html). The scripts are in a form suitable for cluster calculations and use global WALLTIME/STARTTIME parameters. Calculation of the SHG spectrum is set up such that one call of the script in a cluster environment will run one tensor element calculation and then exit. The calculations of the data consist of the following steps:</div> <div> </div> <div>1. Ground-state (gs) calculation:</div> <div> * `chi2-dft/nlo/gs.py` -- plane wave calculations for the `rlx-3R-MoS2-bulk.xyz` structure file using the plane wave mode, 500 eV cutoff, PBE xc-functional. Other parameters are in the `setting.py` file.</div> <div> * Generates the `gs.gpw` file.</div> <div>2. Calculation of unoccupied bands:</div> <div> * Requires finished ground-state calculation.</div> <div> * `chi2-dft/nlo/unocc.py`</div> <div> * This step is optional is the required number of bands calculated in the `gs` step is sufficient.</div> <div> * Generates the `unocc.gpw` file.</div> <div>3. Calculate the matrix elements:</div> <div> * Requires finished ground-state calculation (`gs.gpw`)</div> <div> or</div> <div> * unoccupied bands calculation (`unocc.gpw`)</div> <div> * `chi2-dft/nlo/mommat.py`</div> <div> * Generates the `mml.npz` file.</div> <div>4. Calculation of the SHG spectrum:</div> <div> * Requires the calculated matrix elements (`mml.npz` file).</div> <div> * `chi2-dft/nlo/shg.py` -- computes one chi2 tensor element from the `mml.npz` file. Designed to run in a HPC environment. Will look for the first nonexistent `abc` tensor element in folder and calculate it. Rerun the script to calculate additional ones or rewrite the script to calculate all required elements in one loop.</div> <div> </div>
Data for "Hofstadter spectrum of Chern bands in twisted transition metal dichalcogenides"
<p>Data used to generate each Figure is in files denoted fig....hdf5. <br>These files contain results for multiple parameter values. The group "data" can be read into a pandas dataframe, providing an index and scalar data.<br>Vector valued data is stored in individual datasets, indexed by the index in "data" along 0-th axis.<br>For an example readout, see "example.py".</p> <p>See README.txt for notes on individual files.</p>
Light-Induced Polaronic Crystals in Single-Layer Transition Metal Dichalcogenides
<p>Publication set for Light-Induced Polaronic Crystals in Single-Layer Transition Metal Dichalcogenides,</p> <p>Nano Letters <span>2024</span><span>, 24</span><span>, 42</span><span>, 13179–13184, <a title="DOI URL" href="https://doi.org/10.1021/acs.nanolett.4c03065">https://doi.org/10.1021/acs.nanolett.4c03065</a></span></p> <p> </p>
Spatial separation of spin currents in transition metal dichalcogenides
<p>Code and data from the manuscript "Spatial separation of spin currents in transition metal dichalcogenides"</p>
Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers
<p>Data sets used in "Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers" by Van Winkle and Craig et al.</p> <table> <tbody> <tr> <td>File name</td> <td>Appearance in Manuscript</td> <td>Material</td> <td>P/AP</td> <td>Twist Angle (°)</td> <td>Hetstrain (%)</td> <td>Scan Shape (pixels)</td> <td>Pixel Size (nm)</td> <td>Smoothing Sigma</td> </tr> <tr> <td>DS1</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>0.369</td> <td>0.451</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS2</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>0.523</td> <td>0.138</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS3</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.849</td> <td>0.546</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS4</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.814</td> <td>0.453</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS5</td> <td>Fig. 2m, 2n, 5a, 5d</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.769</td> <td>0.472</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS6</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.721</td> <td>0.849</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS7</td> <td>Fig. 2a, 2d, 2g, 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.227</td> <td>0.304</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS8</td> <td>Fig. 2m, 2n, 5b, 5e</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.644</td> <td>0.970</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS9</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.353</td> <td>0.597</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS10</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.597</td> <td>0.930</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS11</td> <td>Fig. 5c, 5f</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.919</td> <td>1.320</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS12</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.193</td> <td>0.581</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS13</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>0.856</td> <td>0.619</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS14</td> <td>Fig. 1b, 1c, 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>0.818</td> <td>0.319</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS15</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>2.562</td> <td>0.444</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS16</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>2.121</td> <td>0.595</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS17</td> <td>Fig. 5h, 5k</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.533</td> <td>1.353</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS18</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>2.697</td> <td>0.455</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS19</td> <td>Fig. 5i, 5l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.459</td> <td>1.138</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS20</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>0.514</td> <td>0.434</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS21</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>0.652</td> <td>0.500</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS22</td> <td>Fig. 2c, 2f, 2i, 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.688</td> <td>0.588</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS23</td> <td>Fig. 2k, 2l, 5g, 5j</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.361</td> <td>0.620</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS24</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.226</td> <td>0.472</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS25</td> <td>Fig. 1d, 2b, 2e, 2h, 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>0.771</td> <td>0.380</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS26</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.483</td> <td>0.568</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS28</td> <td>Fig. 1b, 1c, 3c, 3f, 3j</td> <td>WSe2/MoS2</td> <td>P</td> <td>0.795</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS29</td> <td>Fig. 1d</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.070</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS30</td> <td>Fig. 4g, 4j</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.776</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS31 (and raw dm4 data)</td> <td>Fig. 4h, 4k</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.852</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS32</td> <td>Fig. 3b, 3e, 3i</td> <td>WSe2/MoS2</td> <td>AP</td> <td>1.072</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS33</td> <td>Fig. 4i, 4l</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.824</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS34</td> <td>Fig. 4a, 4d, 4g, 4j</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.097</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS35</td> <td>Fig. 4b, 4e, 4h, 4k</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.096</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS36</td> <td>Fig. 3a, 3d, 3h</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.127</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS37</td> <td>Fig. 4c, 4f, 4i, 4l</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.167</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> </tbody> </table>
Probing the long-lived photo-generated charge carriers in transition metal dichalcogenides by time-resolved microwave photoconductivity
<p>Raw data of time resolved microwave conductivity measurements. </p>
Data from: Exfoliated transition metal dichalcogenide nanosheets for supercapacitor and sodium ion battery applications
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Imaging of twist angle in 2D transition metal dichalcogenide bilayers
<p>Second harmonic generation imaging of twist angle in 2D transition metal dichalcogenide bilayers</p>
Raw NMR data for Structural Studies of Alloyed and Nanoparticulate Transition Metal Dichalcogenides by Selenium-77 Solid-State Nuclear Magnetic Resonance Spectroscopy
<p>Raw NMR data for main text figures.</p>
Coherent, atomically thin transition-metal dichalcogenide superlattices with engineered strain
<p>The sample is coherent WS<sub>2</sub>-WSe<sub>2</sub> superlattice (Xie, et al. Science 359, 1131-1136 (2018)). The datasets were collected by electron microscope pixel array detector (EMPAD) under the condition described in this paper (Han, et al. Nano Letters, 18, 3746-3751 (2018)). The rotation angle between the real space and diffraction space in these datasets is 37 degrees. The data have also been analyzed in our recent paper (arXiv:2111.06496) and a conference proceeding (Shi, et al. Microsc. Microanal. 27 Suppl 1, 2021). </p>
Understanding and predicting adsorption energetics on monolayer transition metal dichalcogenides
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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.
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.
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.
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.
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.