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11
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ShareScore release 0.9.0
Dataset results
11 results for “Computational chemistry”
QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods
<p>This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. <em>J. Phys. Chem. A</em> <strong>2023</strong>, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes:</p> <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul> <p> </p>
Data for Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization
<p>Dataset substantiating the claims in <a href="https://arxiv.org/abs/2212.07957">[2212.07957] Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization (arxiv.org)</a></p>
Automating Computational Chemistry in Multiscale Catalysis Electronic Data Compendium
<p>Electronic data compendium containing supplemental datasets and figures, primarily detailing lateral interactions and adlayer properties at catalytic surfaces.</p> <p>Part of the physical print of the thesis "Automating Computational Chemistry in Multiscale Catalysis" by B. Klumpers.</p>
Dataset for the article "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry"
<p>This dataset contains additional material related to the article: "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry". The preprint is available at <a href="https://doi.org/10.26434/chemrxiv.7635986">https://doi.org/10.26434/chemrxiv.7635986</a>. Final article is available at <a href="https://doi.org/10.1021/acs.jctc.9b00093">https://doi.org/10.1021/acs.jctc.9b00093</a>.</p>
Facile fabrication of polymer network using click chemistry and their computational study
Open the record for dataset details and reuse information.
Data Supporting GoodVibes: automated thermochemistry for heterogeneous computational chemistry data
<p>These files are provided in support of the use case in a recent manuscript showing the use of the Python package <a href="https://github.com/bobbypaton/GoodVibes">GoodVibes</a>.</p> <p>This data set contains Gaussian optimization and frequency calculations on 25 molecules, along with 25 corresponding ORCA single point energy calculations, a YAML file to dictate and format the reaction pathway, and example outputs of the tabulated thermochemistry and a PNG of the potential energy surface graph output.<br> To generate these output files, the command:</p> <pre><code class="language-bash">python -m goodvibes *.log --spc DLPNO --pes PhPy.yaml --graph PhPy.yaml -t 353.15 --imag --invertifreq -5 --media ethanol -c 1 </code></pre> <p>was run in the directory containing the calculation output (.log and .out) files.</p>
Videos of computer vision based recognition/segmentation/classification of materials inside vessels in chemistry lab and other setting
<p>These videos contain materials in vessels in various settings related to the chemistry lab, medical samples and handling liquids in everyday life settings. The region and type of each vessel and material phase found by the computer vision (neural net) are marked in purple, the class of each phase appears above each panel in green (each panel corresponds to a different class). The work is part of the computer vision for the chemistry lab project.</p> <p>For details on the project see:</p> <p><a href="https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004">https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004</a></p> <p>For Details on the videos See:</p> <p><a href="https://www.youtube.com/watch?v=K7I2QJcIyBQ&list=PLRiTwBVzSM3B6MirlFl6fW0YQR4TtQmtJ">https://www.youtube.com/watch?v=K7I2QJcIyBQ&list=PLRiTwBVzSM3B6MirlFl6fW0YQR4TtQmtJ</a></p> <p>Basically, the videos contain process such as pouring, mixing, foam formation precipitation, phase separation, melting freezing, dissolving, etc.., where the region of each vessel and material phase and their type (liquid, solid, powder, suspension, foam) is found by the neural net and marked purple.</p> <p>For code see:</p> <p><a href="https://github.com/aspuru-guzik-group/Computer-vision-for-the-chemistry-lab">https://github.com/aspuru-guzik-group/Computer-vision-for-the-chemistry-lab</a></p> <p> </p> <p> </p> <p> </p>
Programs for chemoinformatics and computational medicinal chemistry
<p>Programs provided herein are a part of a freely available database of annotated compound data sets and software tools developed in our laboratory for chemoinformatics and computational medicinal chemistry. The original release is described in the following article:<br /> <br /> Hu Y, Bajorath J: Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications [v1; ref status: indexed, http://f1000r.es/Mu9krs] <em>F1000Research</em> 2012; 1:11 (doi: 10.12688/f1000research.1-11.v1).<br /> <br /> The updated version containing the data provided herein will be described in a forthcoming data note in <em>F1000Research</em>.</p>
Data sets for chemoinformatics and computational medicinal chemistry
<p>Data sets provided herein are a part of a freely available database of annotated compound data sets and software tools developed in our laboratory for chemoinformatics and computational medicinal chemistry. The original release is described in the following article:<br /> <br /> Hu Y, Bajorath J: Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications [v1; ref status: indexed, http://f1000r.es/Mu9krs] <em>F1000Research </em>2012; 1:11 (doi: 10.12688/f1000research.1-11.v1).<br /> <br /> The updated version containing the data provided herein will be described in a forthcoming data note in <em>F1000Research</em>.</p>
Chemistry and Mass Density of Aluminum Hydroxide Gel in Eco- Cements by Ptychographic X‑ray Computed Tomography
<p>Raw data for: Chemistry and Mass Density of Aluminum Hydroxide Gel in Eco- Cements by Ptychographic X‑ray Computed Tomography </p> <p>doi: http://dx.doi.org/10.1021/acs.jpcc.6b10048</p> <p> </p> <p>Eco-cements are a desirable alternative to ordinary Portland cements because of their lower CO<sub>2</sub> footprints. Ye'elimite-based eco-cements are attracting a lot of interest but most of them exhibit relatively poor mechanical properties. Understanding the reasons for the low performances requires the characterization of features such as mass density of the hydrated mineralogical phases, including the amorphous gel, on the sub-micrometer scale which is very challenging. Here we use ptychographic X-ray computed tomography to provide 3D mass density and attenuation coefficient distributions of eco-cement pastes with an isotropic resolution close to 100 nm allowing to distinguish between mineralogical phases with very similar contrast. In combination with laboratory techniques such as the Rietveld method, <sup>27</sup>Al MAS-NMR and electron microscopies, we report compositions and densities of key components. The ettringite and gel volume distributions have been mapped out in the segmented tomograms. Moreover, we discriminate between an aluminum hydroxide gel and calcium aluminum monosulfate, which have close electron density values. Specifically, the composition and mass density of two aluminum hydroxide gels have been determined: (CaO)<sub>0.04</sub>Al(OH)<sub>3</sub>·2.3H2O with 1.48(3) g∙cm<sup>-3</sup> and (CaO)<sub>0.12</sub>Al(OH)<sub>3</sub> with 2.05(3) g∙cm<sup>-3</sup>, which was a long standing challenge.</p>
Computational Chemistry and Machine Learning-assisted Screening of Supported Amorphous Metal Oxide Nanoclusters for Methane Activation
<p>Gaussian input and output files for reproducibility of the results.</p>
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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.