A Data Science Approach to Estimate Enthalpy of Formation of Cyclic Hydrocarbons
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ArticleAuthors
Yalamanchi, Kiran K.Monge Palacios, Manuel
van Oudenhoven, Vincent C.O.
Gao, Xin

Sarathy, Mani

KAUST Department
Chemical Engineering ProgramClean 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
2020-07-10Online Publication Date
2020-07-10Print Publication Date
2020-08-06Embargo End Date
2021-07-10Permanent link to this record
http://hdl.handle.net/10754/664255
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Show full item recordAbstract
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.0c02785Sponsors
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)Additional Links
https://pubs.acs.org/doi/10.1021/acs.jpca.0c02785ae974a485f413a2113503eed53cd6c53
10.1021/acs.jpca.0c02785