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    A Data Science Approach to Estimate Enthalpy of Formation of Cyclic Hydrocarbons

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    Type
    Article
    Authors
    Yalamanchi, Kiran K.
    Monge Palacios, Manuel
    van Oudenhoven, Vincent C.O.
    Gao, Xin cc
    Sarathy, Mani cc
    KAUST Department
    Chemical Engineering Program
    Clean Combustion Research Center
    Combustion and Pyrolysis Chemistry (CPC) Group
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Physical Science and Engineering (PSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2020-07-10
    Online Publication Date
    2020-07-10
    Print Publication Date
    2020-08-06
    Embargo End Date
    2021-07-10
    Permanent link to this record
    http://hdl.handle.net/10754/664255
    
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    Abstract
    In spite of increasing importance of cyclic hydrocarbons in various chemical systems, fundamental properties of these compounds, such as enthalpy of formation, are still scarce. One of the reasons for this is the fact that the estimation of thermodynamic properties of cyclic hydrocarbon species via cost-effective computational approaches, such as group additivity (GA), has several limitations and challenges. In this study, a machine learning (ML) approach is proposed using support vector regression (SVR) algorithm to predict standard enthalpy of formation of cyclic hydrocarbon species. The model is developed based on a thoroughly selected dataset of accurate experimental values of 192 species collected from the literature. The molecular descriptors used as input to the SVR are calculated via alvaDesc software, which computes in total 5255 features classified into 30 categories. The developed SVR model has an average error of approximately 10 kJ/mol. In comparison, the SVR model outperforms the GA approach for complex molecules, and can be therefore proposed as a novel data-driven approach to estimate enthalpy values for complex cyclic species. A sensitivity analysis is also conducted to examine the relevant features that play a role in affecting the standard enthalpy of formation of cyclic species. Our species dataset is expected to be updated and expanded as new data is available in order to develop a more accurate SVR model with broader applicability.
    Citation
    Yalamanchi, K. K., Monge-Palacios, M., van Oudenhoven, V. C. O., Gao, X., & Sarathy, S. M. (2020). A Data Science Approach to Estimate Enthalpy of Formation of Cyclic Hydrocarbons. The Journal of Physical Chemistry A. doi:10.1021/acs.jpca.0c02785
    Sponsors
    This work was supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under the award number OSR-2019-CRG7-4077, and the KAUST Clean Fuels Consortium (KCFC) and its member companies.
    Publisher
    American Chemical Society (ACS)
    Journal
    The Journal of Physical Chemistry A
    DOI
    10.1021/acs.jpca.0c02785
    Additional Links
    https://pubs.acs.org/doi/10.1021/acs.jpca.0c02785
    ae974a485f413a2113503eed53cd6c53
    10.1021/acs.jpca.0c02785
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Structural and Functional Bioinformatics Group; Computer Science Program; Chemical Engineering Program; Clean Combustion Research Center; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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