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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Machine learning the quantum flux-flux correlation function for catalytic surface reactions

Authors: Brenden G. Pelkie; Stephanie Valleau;

Machine learning the quantum flux-flux correlation function for catalytic surface reactions

Abstract

This dataset contains information on each of the 14 reactions used in the paper, the geometries for these reactions, the product of the quantum reaction rate constant and canonical reactant partition function and the flux-flux correlation function time series values for each reaction-temperature combination. reaction_details.csv This is a .csv file containing additional details on the reactions used in this paper. Each row contains one reaction/temperature combination, of which there are 55. Column descriptions: reaction_number: Reaction identifier number used in this work reaction: The chemical reaction equation metal_surface: atomic symbol of metal surface facet_number: Miller indices of surface reactants: Python dictionary object of reactants and their quantities products: Python dictionary object of products and their quantities reaction_energy [eV]: reaction energy in electron-volts activation_energy [eV]: activation energy of reaction in electron-volts temperature [K]: The randomly assigned temperature a calculation was run for kQ_Cff [1/au]: The calculated integrated reaction rate product at corresponding temperature {1,2,3,4} in units 1/(au time). reaction_split: Train/test placement of that reaction/temperature combination for reaction split temperature_split: Trian/test placement of that reaction/temperature combination for temperature split catalysishub_reactionID: Catalysis Hub reaction ID identifier for referencing catalysis hub database doi: digital object identifier of original publication for which DFT calculations were performed Flux_flux_correlation_functions: Directory containing flux-flux correlation function time series values for each reaction temperature combination. Values are organized in subdirectories, one for each of the 14 reaction. In each subdirectory .csv files are labeled by reaction number and temperature in Kelvin. Each csv file contains a column with time points [au of time] and the corresponding flux-flux correlation function value in units [1/(au of time)2]. Geometries: Directory containing geometry files for each reaction. Geometries of reactants on the surface were shifted respect to those supplied by catalysis hub to create continuous reaction pathways where necessary. Geometry files are organized in subdirectories for each reaction. When complete nudged elastic band (NEB) minimum energy paths (MEP) were not available ,subdirectories contain a products.xyz, reactants.xyz, and TSstar.xyz file (reactions 1 to 11) otherwise the complete set of NEB MEP images labeled neb{n}.xyz is given (reactions 12, 13, 14).

Related Organizations
Keywords

quantum, machine learning, catalysis, kinetics, reaction rate constants, flux-flux correlation function, gaussian process regression

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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