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Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks

Received: 3 May 2016     Published: 4 May 2016
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Abstract

Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descriptors were selected to develop the RBN model. Simulated with the final optimum RBN model [8-35(64)-1], the root mean square errors (RMSE) for the training, the validation and the testing set were 5.55, 4.28, and 5.33, and the correlation coefficients R=0.994(training), 0.994(validation), 0.993(testing). The final RBN model was compared with the multiple linear regression approach and showed more satisfactory results.

Published in Modern Chemistry (Volume 4, Issue 2)
DOI 10.11648/j.mc.20160402.12
Page(s) 24-29
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Radial Basis Networks, Normal Boiling Point, Hydroxyl Compounds, QSPR Model

References
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  • APA Style

    Liangjie Jin, Peng Bai. (2016). Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks. Modern Chemistry, 4(2), 24-29. https://doi.org/10.11648/j.mc.20160402.12

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    ACS Style

    Liangjie Jin; Peng Bai. Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks. Mod. Chem. 2016, 4(2), 24-29. doi: 10.11648/j.mc.20160402.12

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    AMA Style

    Liangjie Jin, Peng Bai. Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks. Mod Chem. 2016;4(2):24-29. doi: 10.11648/j.mc.20160402.12

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  • @article{10.11648/j.mc.20160402.12,
      author = {Liangjie Jin and Peng Bai},
      title = {Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks},
      journal = {Modern Chemistry},
      volume = {4},
      number = {2},
      pages = {24-29},
      doi = {10.11648/j.mc.20160402.12},
      url = {https://doi.org/10.11648/j.mc.20160402.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mc.20160402.12},
      abstract = {Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descriptors were selected to develop the RBN model. Simulated with the final optimum RBN model [8-35(64)-1], the root mean square errors (RMSE) for the training, the validation and the testing set were 5.55, 4.28, and 5.33, and the correlation coefficients R=0.994(training), 0.994(validation), 0.993(testing). The final RBN model was compared with the multiple linear regression approach and showed more satisfactory results.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks
    AU  - Liangjie Jin
    AU  - Peng Bai
    Y1  - 2016/05/04
    PY  - 2016
    N1  - https://doi.org/10.11648/j.mc.20160402.12
    DO  - 10.11648/j.mc.20160402.12
    T2  - Modern Chemistry
    JF  - Modern Chemistry
    JO  - Modern Chemistry
    SP  - 24
    EP  - 29
    PB  - Science Publishing Group
    SN  - 2329-180X
    UR  - https://doi.org/10.11648/j.mc.20160402.12
    AB  - Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descriptors were selected to develop the RBN model. Simulated with the final optimum RBN model [8-35(64)-1], the root mean square errors (RMSE) for the training, the validation and the testing set were 5.55, 4.28, and 5.33, and the correlation coefficients R=0.994(training), 0.994(validation), 0.993(testing). The final RBN model was compared with the multiple linear regression approach and showed more satisfactory results.
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • School of Chemical Engineering and Technology, Tianjin University, Tianjin, PR China

  • School of Chemical Engineering and Technology, Tianjin University, Tianjin, PR China

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