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42 results for “Chemical reactions”
Data set for the journal article ''Nanoscale chemical reaction exploration with a quantum magnifying glass''
<div>This data set includes the raw data of the esterification and hydrogenation discussed in the journal article alongside with the Scine Puffin Singularity container, steering protocol files, Swoose parameters, (pre-)releases of the software, and Python scripts for individual steps without the graphical user interface to reproduce the data.</div>
Computational Supporting Information for How Chemical Environment Activates Anthralin and Molecular Oxygen for Direct Reaction
<p>The updated version of the dataset contains all original computational results, including validation of the level of theory, molecular structures, and analysis spreadsheets that are in support of our experimental observations of spontaneous reactivity of anthralin/dithranol molecule with molecular oxygen without any catalyst or co-substrate.<br> The paper was published in Journal of Organic Chemistry, 2020, 85(2), 1315–1321 (DOI: 10.1021/acs.joc.9b03133).</p> <p>In the meantime, the science was also also presented at the 8th ELSI Symposium, Tokyo Institute of Technology, Tokyo (Japan); February 3-7, 2020 in the context of molecular catalysis and their role in the chemical evolution of the building blocks of life.</p> <p>This version also has an important update that is being exclusively published here on Zenodo. The selected level of theory (MN15 functional with triple-zeta quality basis set supplemented with BOTH diffuse and polarization basis functions) is further confirmed to be one of the most reasonable one among 98 commonly used functionals.</p>
High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions
<p>This Zenodo repository contains the data presented in Spiekermann, K. A.; Pattanaik, L.; Green, W. H.* <a href="https://www.nature.com/articles/s41597-022-01529-6">High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions</a>, Sci. Data 9, 417, (2022). We recommend people refer to this dataset as RDB7 i.e. a diverse reaction database whose transition states contain up to 7 heavy atoms.</p> <p>Atom-mapped SMILES, barrier heights, reaction enthalpies, and Reaction Mechanism Generator (RMG) reaction family for each reaction are listed in the comma-separated values files <strong><em>b97d3.csv</em></strong>, <strong><em>wb97xd3.csv</em></strong>, <strong><em>ccsdtf12_dz.csv</em></strong>, and<em> <strong>ccsdtf12_tz.csv</strong></em>. <em><strong>ccsdtf12_dz_individual_heats_of_formation.csv</strong></em> containing the individual heats of formation for each stable species (i.e., reactant and product). The values in all of these files are in kcal/mol. Q-Chem output files from the reoptimized products are provided for 16,302 reactions at B97-D3/def2-mSVP and for 11,926 reactions at ωB97X-D3/def2-TZVP level of theory. For convenience, these also include the original log files for the reactant, transition state, and non-reoptimized products from Grambow et al. (10.5281/zenodo.3715478) since they were used to calculate barrier heights, enthalpies, and rate constants in this work. The numbering of reaction indices matches that from the originally published dataset to facilitate easy comparison. MOLPRO output files from the single point calculations are provided for 11,926 reactions at the CCSD(T)-F12/cc-pVDZ-F12 level of theory as well as for the 15 validation reactions run at CCSD(T)-F12/cc-pVTZ-F12. The raw log files for all calculations are stored in <strong><em>b97d3.tar.gz</em></strong>, <strong><em>wb97xd3.tar.gz</em></strong>, <strong><em>ccsdtf12_dz.tar.gz</em></strong>, and <strong><em>ccsdtf12_tz.tar.gz</em></strong>. Each archive contains a separate folder for each reaction, which contains log files for the reactant, transition state, and product/s. The Q-Chem log files contain the output from a geometry optimization and harmonic vibrational analysis while the MOLPRO log files contain output from an energy calculation. Transition state theory rate constants, fitted Arrhenius parameters, and average percentage error between the calculated and fitted rate constants can be found for the rigid reactions in <strong><em>ccsdtf12_dz_rigid.csv</em></strong>. The list of 50 temperatures (K) used during Arrhenius fitting is provided in <strong><em>arkane_temperatures.csv</em></strong>, and the raw Arkane outputs are provided in <strong><em>ccsdtf12_dz_rigid.tar.gz</em></strong>.</p> <p>The improvement from fitting bond additivity corrections at B97-D3/def2-mSVP, ωB97X-D3/def2-TZVP, CCSD(T)-F12/cc-pVDZ-F12//ωB97X-D3/def2-TZVP, and CCSD(T)-F12/cc-pVTZ-F12//ωB97X-D3/def2-TZVP is shown in <strong><em>b97d3_def2msvp_BAC.csv</em></strong>, <strong><em>wb97xd3_def2tzvp_BAC.csv</em></strong>, <strong><em>ccsdtf12_ccpvdzf12__wb97xd3_def2tzvp_BAC.csv</em></strong>, and <strong><em>ccsdtf12_ccpvtzf12__wb97xd3_def2tzvp_BAC.csv</em></strong> respectively. The files contain the experimental and calculated enthalpies for the reference species from the RMG-database used for fitting. The correction values are publicly stored on the RMG-database GitHub on the AEC_BAC branch, though they are also provided in <strong><em>fitted_corrections.pkl</em></strong> for convenience. Further validation of the BACs at the double zeta level was done by comparing to experimental values from the Pedley set since over half of these molecules were not in the RMG-database training set used for fitting. The comparison is shown in <strong><em>ccsdtf12_dz_vs_Pedley_experimental.csv</em></strong>.</p> <p> </p>
Data accompanying publication: "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction"
<p>Embeddings and raw files to complement the paper "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction". The embeddings should be all the data needed for full reproducibility of the results published. The GitHub repo GeneralDFT (https://github.com/moleculebits/GeneralDFT) contains the python scripts required to make use of the data, along with some basic plotting functionalities.</p>
Expanding the chemical space using a Chemical Reaction Knowledge Graph
<p>This contains:</p><ul><li>the reaction graph dataset used to train the link prediction model</li></ul><p>Homepage: https://github.com/MolecularAI/reaction-graph-link-prediction</p>
USPTO-LLM: A Large Language Model-Assisted Information-enriched Chemical Reaction Dataset
<p>USPTO-LLM is an <strong>information-enriched chemical reaction dataset</strong> that provides more side information (reaction conditions and reaction steps division) for developing new reaction prediction and retrosynthesis methods and inspires new problems, such as reaction condition prediction. It comprises over <strong>247K chemical reactions</strong> extracted from the patent documents of USPTO (United States Patent and Trademark Office), encompassing abundant information on reaction conditions. </p> <p>We employ large language models to expedite the data collection procedures automatically with a reliable quality control process. The extracted chemical reactions are organized as <strong>heterogeneous directed graphs</strong>, allowing us to formulate a series of prediction tasks, such as reaction prediction, retrosynthesis, and reaction condition prediction, in a unified graph-filling framework.</p>
Metadata of "Effect of chemical substitution on the surface charge of the photosynthetic Reaction Center from Rhodobactersphaeroides: an in-silico investigation"
<p>Metadata of "Effect of chemical substitution on the surface charge of the photosynthetic Reaction Center from Rhodobactersphaeroides: an in-silico investigation"</p>
Data Set for the Journal Article "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks"
<p>This data archive contains all data newly created in the following publication:</p> <p>Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja-<br>Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, and Markus Reiher, "Heron: Visualizing and<br>Controlling Chemical Reaction Explorations and Networks", in preparation.</p> <p>The directory contents are as follows:</p> <ul> <li>steered_eschenmoser.tar.xz: Dump of the database created during the steered exploration</li> <li>steered_exploration_protocol_chemoton_3.1.json: Protocol used for the steered exploration</li> </ul>
Image segmentation masks for curved arrows on molecular images from chemical reaction mechanism images
<p>The dataset presented herein is designed as a ground truth for image segmentation tasks focused on noise extraction in Optical Chemical Structure Recognition (OCSR) processes. It comprises 73 manually extracted and annotated images from real reaction mechanism images, along with 5320 synthetic molecular images generated using RDKit, each featuring computer-drawn curved arrows on random locations on the molecular image pertinent to their respective tasks. Curved arrows are prevalent in chemical reaction mechanism images and significantly impact the accuracy of molecular identity recognition. This dataset aims to enhance OCSR tasks by enabling the pretreatment of molecular images to remove noise, thereby improving molecular recognition accuracy.</p>
Supporting Information for the Journal Article "Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning"
<p>This data set contains all data produced when exploring the Williamson ether synthesis starting from iodoethane and phenol.</p> <p><br> The set is structures as follows:</p> <ul> <li>analysis: Contains the script used to analyze the exploration and the output of said script</li> <li>check_barrier: Contains the output of the manual calculations done to check the barrier of the reaction</li> <li>exploration: Contains the scripts used to initialize and carry out the exploration as well as the two starting structures as XYZ files</li> <li>raw_data: a dump of the MongoDB database with all the data produced during the exploration</li> </ul>
Quantum Chemical Calculations for the Reaction of Methoxide with OTP and CL
<p>DFT calculations at the BP/def2-TZVPD level are executed with Turbomole 7.3 and COSMO-RS calculations with COSMOtherm 19.0.4.</p> <p> </p> <p>One folder is used for one molecule which take part in the following reactions.<br> Educt + Nu- -> Intermediate -> Product<br> Charged system:<br> CL + MeO- -> 2a- -> 3a-<br> OTP + MeO- -> 2b- -> 3b-<br> Neutral system:<br> CL + MeOH -> 2aH -> 3aH<br> OTP + MeOH -> 2bH -> 3bH</p> <p>Folderstructure:<br> opt: Geometric structure optimization with PBEh-3c, frequency analysis<br> sp-vac: Electronic Structure of the vacuum state<br> sp-sol: Electronic Structure of the solute as ideal conductor<br> sp-cosmors: Calculation of solvation enthalpy</p> <p>Quantum Chemical Data Collection (QCDC) creates data.json<br> Further information can be found here:<br> https://github.com/lucasteiner/qcdc<br> </p>
IscoKin database of rate constants for reaction of organic contaminants with the major oxidants relevant to In Situ Chemical Oxidation
<p>Database of second-order rate constants for aqueous phase reaction of organic contaminants with the major oxidants involved in groundwater remediation by In Situ Chemical Oxidation (ISCO)—including permanganate, hydroxyl radical (from activated peroxide), and sulfate radical (from activated persulfate)—plus other comparable oxidants of environmental interest (carbonate radical, chlorine dioxide, and ozone).</p> <p>The data were compiled between 2003 and 2005 by Rachel Waldemer, Jaimie Powell, and other students under the direction of Professor Paul G. Tratnyek at the Oregon Graduate Institute in Beaverton, Oregon, USA. In 2006, a script written by Kaylie Langley was used to make the data available online via an interface that allowed searching by chemical name, CAS-RN, etc. The result was named the “IscoKin” database and was available at http://cgr.ebs.ogi.edu/iscokin/search.php until the server was retired in 2019.</p> <p>In place of IscoKin, we have made all of the data available as a spreadsheet in .xlsx format. The file includes two sheets, the first contains one “recommended” rate constant for each combination of organic compound and oxidant (i.e., one row per compound). The second sheet contains all of the individual rate constants that were compiled (i.e., one row per experimental result).</p> <p>The "best" value is most often an average of the individual values listed in the "All_values_with_references" sheet; but occasionally this value represents a single value, usually the "best" value given in Buxton et al. (1988) “Critical review of rate constants for reactions of hydrated electrons. hydrogen atoms and hydroxyl radicals (•OH/•O-) in aqueous solution” J. Phys. Chem. Ref. Data. 17, 513-886.</p> <p>While the database was checked thoroughly, it still is unlikely to be completely accurate or complete. For critical applications, we recommend tracking down the primary sources (listed in the spreadsheet) and use them for data, conditions, and other caveats. Obviously, we do not accept any responsibility for what anyone does with information obtained from this document.</p> <p>The work was funded by the Strategic Environmental Research and Development Program (SERDP), under grant number ER-1289, titled “Improved Understanding of In Situ Chemical Oxidation”. More details on this work can be found in Part 1 of the final project report, which is located at https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Persistent-Contamination/ER-1289/.</p>
USPTO Dataset for: Fast Chemical Reaction Condition Suggestion via Rule-Based Classification and Similarity Search
<p>USPTO database that is analyzed with Rxn-INSIGHT (<a href="https://github.com/mrodobbe/Rxn-INSIGHT">https://github.com/mrodobbe/Rxn-INSIGHT</a>).</p><p>This gzip file contains a very large Pandas DataFrame that can be loaded via pd.read_parquet('uspto_rxn_insight.gzip'). Because of the large size of the data, PyArrow version 13.0 must be used. </p><p>To use parquet in Pandas, install PyArrow and fastparquet using pip:</p><p>pip install pyarrow==13.0<br>pip install fastparquet</p>
Dataset for Direct Chemical Lithography Writing on 2D Materials by Electron Beam Induced Chemical Reactions
<p>The dataset contains all relevant data and figures regarding the Figure 4, S4 and S5 of manuscript "Direct Chemical Lithography Writing on 2D Materials by Electron Beam Induced Chemical Reactions".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure4a.csv file corresponds to data used to plot graphs from Figure4a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
Chemical reaction between ferropericlase (Mg,Fe)O and water under high pressure-temperature conditions of the deep lower mantle
<p>The XRD datasets and the multigrain dataset for the article "Chemical reaction between ferropericlase (Mg,Fe)O and water under high pressure-temperature conditions of the deep lower mantle" by Yang et al.</p>
Wide-field optical imaging of electrical charge and chemical reactions at the solid-liquid interface
<p>Code availability</p>
Wide-field optical imaging of electrical charge and chemical reactions at the solid-liquid interface
<p>Data availability for polymer measurements</p>
Wide-field optical imaging of electrical charge and chemical reactions at the solid-liquid interface
<p>Data availability of TiO2/SiO2 grid measurements</p>
Capturing chemical reactions inside biomolecular condensates with reactive Martini simulations
<p>Supporting data for publication "Capturing chemical reactions inside biomolecular condensates with reactive Martini simulations". Contains initial and final simulation snapshots of each simulation condition, and the cluster analysis used to determine the macrocycle size.</p> <p> </p>
Supporting Information for Automated and Efficient Sampling of Chemical Reaction Space
<p>These datasets include structures from normal mode sampling, reaction pathway sampling, and transition states for validation (originally from Grambow et al.), all computed using the ωB97X/6-31G(d) method. The corresponding energies and forces are compiled in the Atomic Simulation Environment database format. </p>
ScienceDex guides
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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)
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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.