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Dataset results
9 results for “solid state materials”
A complete picture of cation dynamics in hybrid perovskite materials from solid-state NMR spectroscopy
<p> Raw, collated NMR and XRD data for the article "A Complete Picture of Cation Dynamics in Hybrid Perovskite Materials from Solid-State NMR Spectroscopy". For further details see the readme.txt file.</p>
A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 1)
<p>This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of JSON files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database. </p><p>Here, we also provide the larger computational data JSON files for 700 compounds. This file consists of all important LOBSTER computation output file data stored as a dictionary.</p><p>Rest 820 computational data JSONs are provided as part of the following repository: A Quantum-Chemical Bonding Database for Solid-State Materials (Part 2): <a href="https://doi.org/10.5281/zenodo.8092187">https://doi.org/10.5281/zenodo.8092187.</a> </p><p>This dataset is published as part of our publication: <a href="https://www.nature.com/articles/s41597-023-02477-5">A Quantum-Chemical Bonding Database for Solid-State Materials. </a> Details about the data generation, validation, and metadata description can be found in our publication.</p><p>Additionally, all the scripts and tools used for curating (including metadata description), benchmarking, and reusing our data are documented in the openly accessible repository. This enables one to reproduce the data and results presented in our work fully. These scripts can be accessed from either of the following links:</p><p>Zenodo: <a href="https://doi.org/10.5281/zenodo.8172527">https://doi.org/10.5281/zenodo.8172527</a></p><p>Github: <a href="https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/tree/v1.0.6">https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/ (v1.0.6)</a></p><p>The dataset will also be available through <a href="https://next-gen.materialsproject.org/">The Materials Project</a> soon.</p>
A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 2)
<p>This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database. </p><p>Here, we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output file data stored as a dictionary.</p><p>This dataset is published as part of our publication: <a href="https://www.nature.com/articles/s41597-023-02477-5">A Quantum-Chemical Bonding Database for Solid-State Materials. </a>Details about the data generation, validation, and metadata description can be found in our publication.</p><p>Additionally, all the scripts and tools used for curating (including metadata description), benchmarking, and reusing our data are documented in the openly accessible repository. This enables one to reproduce the data and results presented in our work fully. These scripts can be accessed from either of the following links:</p><p>Zenodo: <a href="https://doi.org/10.5281/zenodo.8172527">https://doi.org/10.5281/zenodo.8172527</a></p><p>Github: <a href="https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/tree/v1.0.6">https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/ (v1.0.6)</a></p><p>The dataset will also be available through <a href="https://next-gen.materialsproject.org/">The Materials Project</a> soon.</p>
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
<p>We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning methods focused on solid-state materials with periodic crystal structures. Applying machine learning methods to solid-state materials is a nascent field with substantial fragmentation largely driven by the great variety of datasets used to develop machine learning models. This fragmentation makes comparing the performance and generalizability of different methods difficult, thereby hindering overall research progress in the field. Building on top of open-source datasets, including large-scale datasets like the OpenCatalyst Project, OQMD, NOMAD, the Carolina Materials Database, and Materials Project, the MatSci ML benchmark provides a diverse set of materials systems and properties data for model training and evaluation, including simulated energies, atomic forces, material bandgaps, as well as classification data for crystal symmetries via space groups. The diversity of properties in MatSci ML makes the implementation and evaluation of multi-task learning algorithms for solid-state materials possible, while the diversity of datasets facilitates the development of new, more generalized algorithms and methods across multiple datasets. In the multi-dataset learning setting, MatSci ML enables researchers to combine observations from multiple datasets to perform joint prediction of common properties, such as energy and forces. Using MatSci ML, we evaluate the performance of different graph neural networks and equivariant point cloud networks on several benchmark tasks spanning single task, multitask, and multi-data learning scenarios. Our open-source code is available at https://github.com/IntelLabs/matsciml.</p>
Supplementary Material to "An improved grand-potential phase-field model of solid-state sintering for many particles"
<p>Supplementary Material to the publication "An improved grand-potential phase-field model of solid-state sintering for many particles" by Seiz, Hierl and Nestler. This contains the pre-study for determining the effective stiffness for the rigid-body velocity calculation and video files showing the 3D evolution of the green body in more detail than possible in the paper itself.</p> <p> </p> <p>slicethru_{start,end}.webm: Moving slices through the 400^3 nm green body at t=0.045ms and t=1.8ms representing the start and end of the simulation respectively. White/transparency indicates the surrounding vapor, with the colourmap showing different grains. Any interfaces are shown as black lines.</p> <p> </p> <p>greenbody_400.webm : Time evolution of the 400^3 nm green body based on the solid-vapor interface. White/transparent indicates the surrounding vapor, with the brownish material indicating the grains.</p> <p> </p> <p>prestudy.zip: Contains the notebook and data used for the pre-study for determining the effective stiffness. A binder is available at</p> <pre>https://mybinder.org/v2/git/https%3A%2F%2Fgit.scc.kit.edu%2Fxt5201%2Fsupplementary-material-for-improved-pf-sintering-model/master?labpath=eval-kvar.ipynb</pre> <p> </p> <p> </p>
Data from: Solid-state high harmonic generation in common large bandgap substrate materials
Open the record for dataset details and reuse information.
Data for "Resolving Structures of Paramagnetic Systems in Chemistry and Materials Science by Solid-State NMR: the Revolving Power of Ultra-Fast MAS"
<p>Raw NMR data</p>
Modeling the electronic structure of organic materials: A solid-state physicist's perspective
<p>electronic and optical properties of anthracene molecules and anthracene crystal structure. <br> </p>
Improving FEM-Based Solid Mechanics Simulations for Ultrashort Pulse Laser Ablation by Integrating an Equation of State and Material Separation
<p>Simulation results (raw data), COMSOL simulation files and Python evaluation scripts for the publication entitled "Two-Temperature Solid Mechanics Model incorporating an Equation of State for Readily Accessible Ultrashort Pulse Laser Ablation Simulations".</p><p> </p><p>Descripton folders:</p><p>Example for nomenclature: SM-EOS_005_F075</p><p>- SM -> solid mechanics (comsol simulation)</p><p>- EOS -> simulation with parameters from EOS, respectively ISO -> simulation with iso-parameters</p><p>- 005 -> pulselength of 0.5 ps</p><p>- F075 -> Fluence: 0.75 * threshold fluence</p><p> </p><p>Structure .txt files:</p><p>- first 8 rows header</p><p>- delemiter = ','</p><p>- first column is the depth in nm, following columns are the Values of the parameter (each column = 0.1 ps time step)</p><p>- start time = 2 * pulselength, end time = 100 ps</p><p> </p><p>Values:</p><p>Electron Temperature T_EL.txt in kK</p><p>Lattice Temperature T_IO.txt in kK</p><p>Pressure P.txt in GPa</p><p>displacement.txt in nm</p><p>Density rho.txt in g/cc</p><p>phase.txt (1 = solid, 2 = solid+liquid, 3 = liquid, 4 = liquid+gas, 5 = gas, negative sign = metastable)</p><p>condTrans.txt (1 = solid and 0 = liquid)</p>
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