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4 results for “chemical accuracy”
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>
Enhancing accuracy of air quality and temperature forecasts during paddy crop-residue burning season in Delhi via chemical data assimilation
<p>This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) generated 72 h fine particulate matter (PM<sub>2.5</sub>) forecasts in Delhi during the crop residue burning season of Oct-Nov 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol-radiation interactions. The assimilation significantly pushes the model AOD and PM<sub>2.5</sub> towards the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF-Chem forecast with MODIS AOD assimilation, aerosol-radiation feedback turned on, and real-time fire emissions reduce the mean bias by 88-195 µg/m<sup>3</sup> (70-86%) with the largest improvement during the peak air pollution episode of 6-13 November 2017. Aerosol-radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third day of PM<sub>2.5 </sub>forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM<sub>2.5</sub> forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol-radiation feedback extends the benefits of assimilating satellite AOD beyond PM<sub>2.5</sub> forecasts to surface temperature forecast with a reduction in the mean bias of 0.9<sup>o</sup>C - 1.5<sup>o</sup>C (17-30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.</p>
Chemperium database for: Geometric Deep Learning for Molecular Property Predictions with Chemical Accuracy Across Chemical Space
<p>The dataset and trained models for the submitted manuscript "Geometric Deep Learning for Molecular Property Prediction with Chemical Accuracy Across Chemical Space"</p> <p>The trained models can be used in combination with the predict module in github.com/mrodobbe/chemperium. More information in README.md.</p> <p><em>When using these datasets, refer directly to the manuscript: https://doi.org/10.1186/s13321-024-00895-0 </em></p>
Data of "Accuracy of predicting chemical body composition of growing pigs by dual-energy X-ray absorptiometry"
<p>Data set for article "Accuracy of predicting chemical body composition of growing pigs by dual-energy X-ray absorptiometry" (DOI). Data set of Swiss Large White entire male pigs for nutrient composition (water, lipid, N, ash, Ca and P) determined by wet-chemistry and body composition (lean mass, body mineral content and fat tissue mass) by dual-energy X-ray absorptiometry (DXA) in the empty body of live pigs and (N=61) in pig carcasses (N=68) within a body weight range from 20 to 100 kg.</p> <p><strong>metadata.xlsx</strong>: description of variables in the data sets</p> <p><strong>emptybody.txt</strong>: corresponds to contents of the empty body at slaughter (three days after DXA live scans) and DXA live scans</p> <p><strong>Carcass.txt</strong>: corresponds to carcass contents and DXA carcass scans on the day of slaughter</p> <p> </p>
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