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.
18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “high entropy alloys”
Example of reverse Monte Carlo simulation for fcc high-entropy alloy CrMnFeCoNi
<p>The data set contains the example of reverse Monte Carlo (RMC) simulation of EXAFS spectra collected for fcc high-entropy alloy CrMnFeCoNi.</p> <p>The simulation was performed by the EvAX code freely available from http://www.dragon.lv/evax/. </p> <p> </p>
Materials for Design Open Repository. High Entropy Alloys
<p>The current dataset is composed of a collection of High Entropy Alloys (HEAs). It contains the alloy composition, the number of chemical elements (No), the phase in a simple form (S_Phase), where 4 classes of phases were considered, namely amorphous (AM), intermetallic (IM), solid solution (SS), and solid solution + intermetallic (SS+IM). It contains also a second phase column (Phase), where we added the type of phase present in alloys with SS and repeated the S_Phase entry for the other cases. We have calculated 13 design parameters (see their definition below) used to design HEAs, known as the parametric approach. Finally, a set of columns containing the chemical elements and their corresponding fraction in the alloy is included. This dataset was developed in the framework of the European project ACHIEF for the discovery of novel materials to be used in industrial processes.</p> <ol> <li>Mean atomic radius <em>a</em> (Å) <ul> <li><span class="math-tex">\(a = \displaystyle\sum_{i=1}^{n} c_i r_i\)</span></li> </ul> </li> <li>Atomic size difference δ <ul> <li><span class="math-tex">\(\delta = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{r_i}{a} \bigg)^2}\)</span></li> </ul> </li> <li>Average melting temperature <em>T<sub>m</sub></em> (K) <ul> <li><span class="math-tex">\(T_m = \displaystyle\sum_{i=1}^{n} c_i T_{mi}\)</span></li> </ul> </li> <li>Average melting temperature standard deviation (K) <ul> <li><span class="math-tex">\(\sigma_{T_m} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{T_{mi}}{T_m} \bigg)^2}\)</span></li> </ul> </li> <li>Mixing enthalpy Δ<em>H<sub>mix</sub></em> (kJ/mol) <ul> <li><span class="math-tex">\(\Delta H_{mix} = 4 \displaystyle\sum_{i \neq j} c_i c_j H_{ij}\)</span></li> </ul> </li> <li>Mixing enthalpy standard deviation (kJ/mol) <ul> <li><span class="math-tex">\(\sigma_{\Delta H_{mix}} = \sqrt{\displaystyle\sum_{i \neq j} c_i c_j (H_{ij} - \Delta H_{mix})^2}\)</span></li> </ul> </li> <li>Ideal mixing entropy <em>S<sub>id</sub></em> (<em>R</em>)<strong>*</strong> <ul> <li><span class="math-tex">\(S_{id} = \Delta S_{mix} = -R \displaystyle\sum_{i=1}^{n} c_i \ln c_i\)</span></li> </ul> </li> <li>Electronegativity <em>χ</em> <ul> <li><span class="math-tex">\(\chi = \displaystyle\sum_{i=1}^{n} c_i \chi_i\)</span></li> </ul> </li> <li>Electronegativity difference in a multi-component alloy system <ul> <li><span class="math-tex">\(\Delta\chi = \displaystyle\sqrt{\sum_{i=1}^{n} c_i(\chi_i - \chi)^2}\)</span></li> </ul> </li> <li>Valence electron concentration <em>VEC</em> <ul> <li><span class="math-tex">\(VEC = \displaystyle\sum_{i=1}^{n} c_i \cdot VEC_i\)</span></li> </ul> </li> <li>Valence electron concentration standard deviation <ul> <li><span class="math-tex">\(\sigma_{VEC} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (VEC_i - VEC)^2}\)</span></li> </ul> </li> <li>Mean bulk modulus <em>K </em>(GPa) <ul> <li><span class="math-tex">\(K = \displaystyle\sum_{i=1}^{n} c_i K_i\)</span></li> </ul> </li> <li>Bulk modulus standard deviation (GPa) <ul> <li><span class="math-tex">\(\sigma_{K} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (K_i - K)^2}\)</span></li> </ul> </li> <li>Young's modulus <em>E</em> (GPa) <ul> <li><span class="math-tex">\(E = \displaystyle\sum_{i=1}^{n} c_i E_i\)</span></li> </ul> </li> <li>Shear modulus <em>G</em> (GPa) <ul> <li><span class="math-tex">\(G = \displaystyle\sum_{i=1}^{n} c_i G_i\)</span></li> </ul> </li> </ol> <p>where <em>n</em> is the number of components in the alloy system, <em>c<sub>i</sub></em> is the stoichiometric ratio, <em>r<sub>i</sub></em> is the atomic radius, <em>T<sub>mi</sub></em> is the melting temperature, <em>χ<sub>i</sub></em> is the Pauli electronegativity, <em>VEC<sub>i</sub></em> is the valence electron concentration, and <em>K<sub>i</sub></em> is the bulk modulus, <em>E<sub>i</sub></em> is the Young's modulus, and <em>G<sub>i</sub></em> is shear modulus for the <em>i</em>-th component of the alloy. <em>H<sub>ij</sub></em> is the binary mixing enthalpy in the liquid phase, and <em>R</em> is the gas constant.</p> <p><strong>*Note:</strong> the ideal mixing entropy <em>S<sub>id</sub></em> units in the first version of the dataset appear as kJ/mol, but they should be written in terms of the gas constant <em>R</em>, e.g., the compound Ag<sub>2</sub>Al has <em>S<sub>id</sub></em> = 0.636 <em>R</em>, where <em>R</em> = 8.314 J · K<sup>−1</sup> · mol<sup>−1</sup>. The second version the <em>S<sub>id</sub></em> units are corrected and two new features are included.</p>
Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting
<p>Atomic structure data used in the research article entitled "Small Dataset Machine-Learning Approaches to Explore the Design Space of High-Entropy Alloys: Engineering ZnTe-based Multicomponent Alloys for the Photo-Splitting of Water"</p>
Data for a publication "The role of the preparation route on microstructure and mechanical properties of AlCoCrFeNi high entropy alloy"
<p>A dataset containing data for the published article "The role of the preparation route on microstructure and mechanical properties of AlCoCrFeNi high entropy alloy".</p> <p> </p> <p>For more details, please read the <strong>README Description of data and analysis.txt</strong> file.</p> <p> </p> <p> </p>
Alloy Sustainability Database - Evaluating the Economic, Environmental, and Societal Impacts of High-Entropy Alloys
<p>This database encompasses 18 elements and details over 400 high entropy alloys (HEAs), Ni-based superalloys, and steels, providing an extensive review of their properties and capabilities. To assess the broader implications of these materials, we have developed nine indicators that evaluate their economic, environmental, and human health impacts. Each indicator is meticulously described, including the methods used for their calculation. This data has been compiled with the goal of integrating considerations of societal impact into the alloy design process, thereby promoting the development of materials that are not only innovative but also socially responsible.</p>
Kinoson data for FCC random and high entropy alloys
<p>This database contains the single KMC-step datasets for the "Ordered" and "Random" rate lattice gas systems, as well the "Complex" high-entropy alloy to accompany our main publication along with our code repository mentioned therein. The datasets are given as HDF5 files, readable with the `h5py` module in python 3. These files are listed below:</p><p>* CrystalData: `CrystData.h5` and `CrystData_ortho_5_cube.h5` contain all the necessary FCC crystal structure data for primitive 8x8x8 and orthogonal 5x5x5 FCC supercells.</p><p>* Lattice_gas/2-component/Ordered_rate: `singleStep_FCC_SR2_c0_X_Run2.h5` are the datasets for the 2-component "Ordered" rate lattice gas systems</p><p>* Lattice_gas/2-component/Random_rate: `singleStepFCC_CR2_c0_X_Run_3.h5` are the datasets for the 2-component "Random" rate lattice gas systems, with X = 60, 70, 75, 80, 85 for the slow species concentration. In these datasets, the slow and fast species have integer labels of 0 and 1, and the vacancy has an integer label of 2.</p><p>* Lattice_gas/5-component/Ordered_rate: `singleStep_FCC_5comp_LG_EquiComp_T.h5` are the datasets for the 5-component "Ordered" rate lattice gases</p><p>* Lattice_gas/5-component/Random_rate: `singleStep_HEA_dist_EquiComp_T.h5` are the datasets for the 5-component "Random" rate lattice gases, with T = 1073, 1173, 1273, 1373 for the simulated temperatures. In these datasets, the four slow species have integer labels 0, 1, 2 and 3, while the fast species has an integer label of 4, and the vacancy has an integer label of 5.</p><p>* HEA_MEAM: `singleStep_HEA_MEAM_T_ftol_1e-3.h5` (T = 773, 1073, 1173, 1273, 1373) contain the single KMC step samples for the complex high-entropy alloy simulated with the MEAM potential. In these datasets, the vacancy has an integer label of 0, while Co, Ni, Cr, Fe and Mn have integer labels 1, 2, 3, 4 and 5.</p><p>Along with the datasets, in each directory, the optimal neural network models for each system have also been provided as PyTorch "state dictionaries", along with the optimal neural network-predicted relaxation vectors. To illustrate the use of these datasets, neural networks and their relaxation vectors, python 3 Jupyter notebooks have also been provided to show the calculation of the transport coefficients using the Scaled Bias Basis (SBB) Method for all systems, as well as the NN+SRBC method for the "Complex" high-entropy alloy. In each directory, these notebooks have been numbered in their file names so as to make it easier to navigate through them. Along with such examples and the datasets, source codes for our neural network models and cluster expansion models are available in the code repository mentioned in our main text. Modules in this repository are also required to run these example notebooks.</p><p> </p>
Data for "Competition between phase ordering and phase segregation in the Ti$_x$NbMoTaW and Ti$_x$VNbMoTaW refractory high-entropy alloys"
<p>Data associated with the arXiv preprint: "Competition between phase ordering and phase segregation in the Ti$_x$NbMoTaW and Ti$_x$VNbMoTaW refractory high-entropy alloys". Version 2 corrects an error in the files associated with fitted atom-atom interaction energies.</p>
Data of "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework"
<p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework",<br> journal = "Engineering Fracture Mechanics",<br> year = "2022",<br> volume = "275",<br> pages = "108844 ",<br> doi = "https://doi.org/10.1016/j.engfracmech.2022.108844",<br> author = "Antoine Hilhorst, Julien Leclerc, Thomas Pardoen, Pascal J. Jacques, Ludovic Noels, Van-Dung Nguyen"</p> <p>New version following review.</p> <p> </p> <p> </p>
Data for "Structure, short-range order, and phase stability of the Al$_x$CrFeCoNi high-entropy alloy: Insights from a perturbative, DFT-based analysis"
<p>Data associated with "Structure, short-range order, and phase stability of the AlxCrFeCoNi high-entropy alloy: Insights from a perturbative, DFT-based analysis", published in npj Comput. Mater. <strong>10</strong>, 271 (2024).</p>
Data for a publication "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys"
<p>Dataset contains data that has been used within the manuscript entitled: "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys". For more information, please read the README.txt file.</p>
Microstructure and mechanical properties of mechanically-alloyed CoCrFeNi high-entropy alloys using low ball-to-powder ratio
<p>The main issue of this work was to analyse the microstructural evolution and mechanical properties of FCC high entropy alloy (HEA) when BPR (ball-to-powder ratio) was limited to 5:1. The motivation of our work is to increase the amount of milled fraction without losing efficiency of the milling process. Nowadays many papers describe HEAs by using powder metallurgy processes, but higher BPR is used. In consequence less amount of powder is milled in one period and the process is not effective enough from the industrial point of view.</p> <p>In this work four equiatomic CoCrFeNi samples were made by Mechanical Alloying plus Spark Plasma Sintering using different milling times: 10, 20, 30, 40 hours. We used 200 Φ5 mm WC balls and milled with intervals 15:15 minutes. Milling speed was 250 rpm. After the mechanical alloying has been finished samples were sintered by using Spark Plasma Sintering technique. We chose 950 °C as a process temperature with heating rate 100 °C/min. Sintering pressure was 50 MPa. Samples were then homogenise in 1050 °C for 12 hours. Then samples were water quenched.</p> <p>The densification of samples during sintering was in satisfied level, what was confirmed by relative densities of samples (>90 %) The microstructure observation of sintered samples revealed Cr-rich particles evenly distributed in samples volume. The number of particles decreases with increasing the milling time. Elements are randomly distributed in the matrix phase except a small Cr-depletion. XRD technique shows multiple FCC structure. As the milling time exceeds, the main FCC structure is promoted. Microhardness increased as a function of milling time. After annealing microstructures were almost out of Cr-rich phase. Only the biggest particles remained. EBSD revealed the grain size decrement as a function of milling time. Also X-ray diffractograms presented significant homogenisation of manufactured samples. Despite the microhardness decrease after heat treatment, the longest milled sample still possess very promising properties. Moreover hardness of samples is not indent’s size dependent (micro- and nanohardness).</p> <p>During milling time the particles are joining and fracturing many times. As a consequence elements are mixing and promoting the new phase(s) growing. We deduced that Hall-Petch effect is the most important factor determining better mechanical properties in longer milled samples. However the milling process need to be improvement. Cr-rich phase observed in sintered samples is the effect of low efficiency of the process, which might be improved by either smaller fraction of Cr at the beginning (premilling process) or increase the other process parameters (milling speed, sintering time).</p>
Supplementary material for "Shock-induced spallation in a nanocrystalline high-entropy alloy: An atomistic study"
<p>Data provided in this upload:</p> <ul> <li>Pictures and tables from "Shock-induced spallation in a nanocrystalline high-entropy alloy: An atomistic study" paper</li> <li>Excel-Charts used to create the graphs and tables</li> <li>Vzz data files from our dump files extracted using OVITO used to calculate the SWVs (position datapoints are the crucial part)</li> <li>The potential used for our simulations</li> <li>Readme with the following information: <ul> <li>Crucial parameters used in our simulations</li> <li>Captions of all the figures and tables used in order</li> <li>Additional information in regards to shock wave velocities (SWV) and their calculation, plus the necessary values</li> </ul> </li> </ul>
Automatic exhaustive calculations of large material space by Korringa-Kohn-Rostoker coherent approximation method --- Applied to equiatomic quaternary high entropy alloys
<p>Calculated data of equiatomic quaternary solid solution phase (high-entropy alloys) on local magnetic moment, total magnetization, magnetic phase transition temperature and residual resistivity.</p> <p>The data was added on October 28.</p>
Raw Data for Evidence for Isotropic s-Wave Superconductivity in High-Entropy Alloys
<p>Raw Data for the paper "Evidence for Isotropic s-Wave Superconductivity in High-Entropy Alloys".</p>
A Library of High-Entropy-Alloy Nanocrystals with Controlled Surface Atomic Arrangements for Catalysis
<p>Input file of PtIrRuRhPd on Pd model based on SQS theory and Data of DFT calculations</p>
Partial liquid metal dealloying to synthesize nickel-containing porous and composite ferrous and high-entropy alloys
<p>Raw data for the graphs in the article.</p>
ULtrahigh TEmperature Refractory Alloys (ULTERA) Database of High Entropy Alloys
<p>ULTERA database, developed under the <a href="https://arpa-e.energy.gov/?q=arpa-e-programs/ultimate">ARPA-E's ULTIMATE program</a>, is aimed at collecting literature data on high entropy alloys (HEAs) to facilitate rapid ML-based discovery of new ones using forward and inverse design.</p> <p>The main scope of this dataset is collecting data on compositionally complex alloys (CCAs), also known as high entropy alloys (HEAs) and multi-principle-element alloys (MPEAs), with extra attention given to (1) high-temperature (refractory) mechanical data, (2) phases present under different processing conditions. Although low-entropy alloys (incl. binaries) are typically not presented to the end-user (or counted in statistics), some are present and used in ML efforts; thus, all high-quality alloy data contributions are welcome! You can set up a contribution in as little as few minutes with <a href="https://contribute.ultera.org">this contribution repository at contribute.ultera.org</a></p> <p>As of September 2024, ULTERA contains over:</p> <ul> <li>7,900+ property-datapoints, corresponding to</li> <li>3,000+ unique HEAs, collected from</li> <li>570+ unique DOIs.</li> </ul> <p>All data is available through a high-performance API, following FAIR principles, while statistics on it can be found at our <a href="https://ultera.org">ultera.org project web page.</a> The database architecture is designed to automatically integrate starting literature data in real time with methods such as experiments, generative modeling, predictive modeling, and validations.</p> <p>Beyond large size, ULTERA has further advantage of being highly curated with many steps of data validation and then processed through <a href="https://pyqalloy.ultera.org">our abnormal data detection tools (pyqalloy.ultera.org).</a></p>
Supplementary data: "Challenges in automated high-throughput ab initio thermodynamics of magnetic high-entropy alloys"
<p>These supplementary data contain input and output files of EMTO density-functional theory (DFT) calculations and IPython/Jupyter notebooks that were used in analyzing the data for the paper "Challenges in automated high-throughput ab initio thermodynamics of magnetic high-entropy alloys".</p> <p>The data are organized as follows:</p> <ul> <li><em>alloy_discovery</em> folder contains the analysis notebooks and the processed EMTO DFT output data in a h5 database. <ul> <li>An installation of Python and Jupyter notebooks is required to run the notebooks. A recommended way of installing them is the Anaconda Python distribution: https://www.anaconda.com/download/</li> <li>It is recommended to first run the Requirements notebook, which checks what Python dependencies are needed to be downloaded.</li> <li>DFT output data is stored as an HDF5 database is located in the <em>results</em> subfolder.</li> <li>Figures generated with the notebooks are located in the <em>figures </em>subfolder.</li> </ul> </li> <li><em>output_files </em>folder contains raw output files of EMTO DFT calculations.</li> <li><em>input_files </em>folder contains raw input files that were used in the EMTO DFT calculations.</li> </ul>
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.