Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “first-principles calculations”
Revised direct band gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment
<p>Raw data and plotting scripts associated with the paper:<br><br>Cónal Murphy, Eoin P. O'Reilly and Christopher A. Broderick, "Revised direct band-gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment", <em>APL Mater.</em> (2024) (undergoing revision)<br><br>Tyndall National Institute, Lee Maltings, Dyke Parade, University College Cork, Cork T12 R5CP, Ireland<br>School of Physics, University College Cork, Cork T12 YN60, Ireland</p>
(DATASET) Functionalized boron–nitride nanotubes: First-principles calculations.
<p>Boron nitride nanotubes (BNNTs), a type of nanomaterial that was first made in 1995, have gained attention in the last two and a half decades, having been characterized as promising inorganic nanoparticles for the application and development of devices in nanomedicine (such as drug delivery systems), and chemical sensors, among other applications. With the use of density functional theory (DFT) calculations, we study the effect of functionalizing the surface of a BNNT with hydroxyl (-OH) and carboxyl (-COOH) organic groups at the N and B sites. The results indicate that the -OH radical remains adsorbed in the B site whereas it is detached from the surface when added to the N site. Very differently, the -COOH remains attached for both adsorption sites. Both functionalizations induced a magnetic moment, produced spin-polarized electronic bands, created flat impurity-like bands, decreased the band gaps, and modified the electron density of the nanotubes. All these changes can be used to control and modify the interaction of BNNTs with other molecules of interest like drugs, heavy metals, and greenhouse gasses, among other substances.</p>
The dataset of covalent bond lengths resulting from the first-principle calculations
<p>Optimized geometries (<strong>geoms_b973c_optimized.tar.gz</strong>, <strong>geoms_pbeh3c_optimized.tar.gz</strong> ),</p> <p>sample MP2 single point input files (<strong>mp2singlePointOnB3lypInputs.tar.gz</strong>) based on initial geometries (B3LYP-optimized as in PubChemQC),</p> <p>JANPA output files (<strong>janpa_b973cGeoms.tar.gz</strong>, <strong>janpa_b3lypGeoms.tar.gz</strong>, <strong>janpa_pbeh3cGeoms.tar.gz</strong> ),</p> <p>and files for the covalent bond lengths dataset:</p> <p><strong>selected_mols.txt</strong> - identifiers of molecules with method-independent connectivity, unique InChI and selected as being non-aromatic;</p> <p>numbers of lone pairs (LP) CLPOs for atoms ( <strong>LPs_b3lyp</strong>, <strong>LPs_B97-3c</strong>, <strong>LPs_PBEh-3c</strong> );</p> <p>numbers of bonding (BD) CLPOs for atoms (<strong> BDs_b3lyp</strong>, <strong>BDs_B97-3c</strong>, <strong>BDs_PBEh-3c </strong>);</p> <p><strong>locBonds_b3lyp.txt</strong> , <strong>locBonds_b97-3c.txt</strong> , <strong>locBonds_pbeh-3c.txt</strong> - the datasets of the bonds not affected by electron delocalization. Each line in each file has the following format:</p> <p><em>Element Element BondMultiplicity MoleculeId AtomId1 AtomId2</em></p> <p> </p>
Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures
<p><br /> This work demonstrates how databases of diffusion-related properties can be developed from high-throughput ab initio calculations. The formation and migration energies for vacancies of all adequately stable pure elements in both the face-centered cubic (fcc) and hexagonal close packing (hcp) crystal structures were determined using ab initio calculations. For hcp migration, both the basal plane and z-direction nearest-neighbor vacancy hops were considered. Energy barriers were successfully calculated for 49 elements in the fcc structure and 44 elements in the hcp structure. These data were plotted against various elemental properties in order to discover significant correlations. The calculated data show smooth and continuous trends when plotted against Mendeleev numbers. The vacancy formation energies were plotted against cohesive energies to produce linear trends with regressed slopes of 0.317 and 0.323 for the fcc and hcp structures respectively. This result shows the expected increase in vacancy formation energy with stronger bonding. The slope of approximately 0.3, being well below that predicted by a simple fixed bond strength model, is consistent with a reduction in the vacancy formation energy due to many-body effects and relaxation. Vacancy migration barriers are found to increase nearly linearly with increasing stiffness, consistent with the local expansion required to migrate an atom. A simple semi-empirical expression is created to predict the vacancy migration energy from the lattice constant and bulk modulus for fcc systems, yielding estimates with errors of approximately 30%.</p> <p>Files:</p> <p>figure_excel_files.zip:</p> <p>Excel files for figures in the publication, and excel files of main data tables for FCC and HCP vacancy formation energies and vacancy migration energies.</p> <p>fcc_hvf_hvm.tar.gz and hcp_hvf_hvm.tar.gz:</p> <p>Raw VASP files corresponding to FCC and HCP vacancy formation energies and vacancy migration energies.<br /> <br /> bulk_modulus.tar.gz:</p> <p>Raw VASP files corresponding to FCC bulk modulus calculations.<br /> </p>
Interpretation of H2‑TPR from Cu-CHA Using First-Principles Calculations
<p>Dataset related to the article with the same title and authors:</p> <p><a href="https://doi.org/10.1021/acs.jpcc.3c07998">Interpretation of H2‑TPR from Cu-CHA Using First-Principles Calculations</a></p> <p> </p> <p>The zip file contains selected structures and a README.txt file with additional information.</p>
Energy and forces annotated atomic structures of platinum using first-principles calculations
<p>Dataset for training machine learning potential of platinum.</p> <p>Detailed explanation of data generation is described in the preprint. below</p> <p>Chun, H., Kang, J., Kang, D., Heo, J., Cho, H., Heo, J., … & Han, B. (2022). Tracking the 3d atomic structures during thermal treatment and catalytic activity of individual pt nanoparticles.. https://doi.org/10.21203/rs.3.rs-1441062/v1</p>
Data for "Insight into Ideal Shear Strength of Ni-based Dilute Alloys using First-Principles Calculations and Correlational Analysis"
<p><strong>Summary: </strong></p> <p>This dataset contains inputs, outputs, and analysis needed to replicate the results discussed in a published article. First-principles results were obtained through VASP and an external optimizer while the crystal plasticity finite element calculations were carried out by Abaqus and an existing subroutine. See the manuscript for more details.</p> <p><strong>Folders: </strong></p> <ul> <li>NiX_data-summary ("analysis" archive): a summary of the output of the DFT calculations, including plaintext and numpy binary versions of the elastic constants and ideal shear stresses.</li> <li>NiX_analysis ("analysis" archive): the data analysis scripts needed to reproduce correlation results (a separate calculation in Matlab) and plot figures.</li> <li>NiX_data-detailed ("data" archive): input files for three pre-strain conditions (0.00, 0.15, and 0.30) and all alloying elements, with common files being separated to a top-level folder of their own to limit repetition.</li> </ul> <p><strong>Article: </strong></p> <p>Published: <a href="https://doi.org/10.1016/j.commatsci.2022.111564">10.1016/j.commatsci.2022.111564</a></p> <p>@article{shimanek2022NiX, title = {Insight into ideal shear strength of Ni-based dilute alloys using first-principles calculations and correlational analysis}, journal = {Computational Materials Science}, volume = {212}, pages = {111564}, year = {2022}, issn = {0927-0256}, doi = {https://doi.org/10.1016/j.commatsci.2022.111564}, author = {John D. Shimanek and Shun-Li Shang and Allison M. Beese and Zi-Kui Liu}}</p> <p>Preprint: <a href="https://doi.org/10.48550/arXiv.2108.06412">10.48550/arXiv.2108.06412</a></p>
Methodological Issues in First-Principle Calculations of CH3NH3PbI3 Perovskite Surfaces: Quantum Confinement and Thermal Motion. [Dataset]
<p>Dataset of the simulated ab initio molecular dynamics (AIMD) trajectories of the (2 × 2) × 6 slabs for MAPbI<sub>3</sub> (001)-PbI<sub>2</sub> and MAPbI<sub>3</sub> (001)-MAI slabs of the MAPbI<sub>3</sub> tetragonal phase. The calculations were performed with the CP2K code (V7.1), evaluating the forces with the PBE functional with the Grimme correction scheme (DFT- D3, Zero–damped correction). The trajectory productions include up to 40 ps using the microcanonical ensemble with 0.5 fs of time-step, considering 5 ps of thermalization time. More details in the article support information.</p> <p>For each surface, the dataset includes (i) AIMD trajectories (positions and energies), (ii) Electronic structure (PBE+SOC, calculated with VASP code) along the dynamics considering 270 representative configurations, (iii) Geometry and electronic structure of the 0K slab models.</p>
First-principles calculations on Aurivillius oxides SrBi2(Ta,Nb)2O9
Open the record for dataset details and reuse information.
Data from: First-principles calculations of structural, electronic, magnetic and elastic properties of Mo2FeB2 under high pressure
The structural and electronic density of states, magnetic, and elastic properties of Mo2FeB2 under high pressure have been investigated with first principles calculations. Furthermore, the thermal dynamic properties of Mo2FeB2 were also studied with the quasiharmonic Debye model. The volume of Mo2FeB2 decreases with the increasing pressure. Using the analysis of the density of the states, atom population and Mulliken overlap population, it is observed that as the pressure increases, the B-B bonds are strengthened, and the B-Mo covalency decreases. Moreover, for all pressures, Mo2FeB2 is detected in the antiferromagnetic phase and the magnetic moments decrease with the increasing pressure. The calculated bulk modulus, shear modulus, Young's modulus, Poisson's ratio and universal anisotropy index all increase with the increasing pressure. From thermal expansion coefficient analysis, it is found that Mo2FeB2 shows good volume invariance under high pressure and temperature. The examination of the dependence of heat capacity on the temperature and pressure shows that heat capacity is more sensitive to temperature than to pressure
Data from: First-principles calculations of structural, electronic, magnetic and elastic properties of Mo2FeB2 under high pressure
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.