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Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks

Received: 26 April 2014     Accepted: 26 May 2014     Published: 30 May 2014
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Abstract

So far the methods used to predict or calculate the melting point of organic compunds do not focus on the compound nature, they mostly use microscopic physio-chemical properties of materials. In this paper the disadvantage of such traditional methods will be defined. Then a new method is introduced. This method uses the nature properties of compounds to estimate their melting point based on an artificial neural network and offsets the disadvantges of pervious ones.

Published in Modern Chemistry (Volume 2, Issue 2)
DOI 10.11648/j.mc.20140202.12
Page(s) 15-18
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), 2014. Published by Science Publishing Group

Keywords

Artificial Neural Networks, Neurons, Matlab 2013, Fitnet Function, Levenberg-Marquart Algorithm

References
[1] Gani, J.M.a.R., Group-Contribution Based Estimation of Pure Component Properties. Fluid Phase Equilibria, 2001: p. 183-208.
[2] Rahman, J.C.d.a.M.H., QSAR Approach To The Prediction Of Melting Points Of Substituted Anilines. 6th Int. Conf. on Mathematical Modlling, 1988. 11: p. 843-846.
[3] WANG Qiang, M.P.a.N.S., Position Group Contribution Method for Estimation of Melting Point of Organic Compounds. Chinese Journal of Chemical Engineering, 2009. 17: p. 468-472.
[4] Paula, P.A.a.J.d., Physical Chemistry. 2010: Freeman , NewYork.
[5] Aalae Alkhalil , J.B.N., Clive J. Roberts , Jonathan W. Aylott , and Jonathan C. Burley, Confocal Raman Microscope Mapping of a Kofler Melt. Crystal Growth and Design - ACS Publications, 2011: p. 422-430.
[6] Fangyou Yana, S.X., Qiang Wangb, Zhen Yanga, Peisheng Maa, Predicting the melting points of ionic liquids by the Quantitative Structure Property Relationship method using a topological index. The Journal of Chemical Thermodynamics, 2013. 62: p. 196-200.
[7] Jonsdottir, J.D.D.a.S.O., QSPR Models For Various Physical Properties of Carbo-hydrates Based on Molecular Mechanics and Quantum Chemical Calculations. Carbohydrate Re-search, 2004. 339: p. 269-280.
[8] Handbook of Chemistry and Physics. 84 ed. 2004: CRC Press.
[9] Menhaj, M.B., Fundamentals of Neural Networks. Vol. Computational Intelligence (vol.1). 2012.
[10] Yekta, A.F., Guidance of MATLAB7. 2005.
Cite This Article
  • APA Style

    Yahya Hassanzadeh-Nazarabadi, S. Majed Modaresi, S. Bahram Jafari, Sanaz Taheri-Boshrooyeh. (2014). Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks. Modern Chemistry, 2(2), 15-18. https://doi.org/10.11648/j.mc.20140202.12

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    ACS Style

    Yahya Hassanzadeh-Nazarabadi; S. Majed Modaresi; S. Bahram Jafari; Sanaz Taheri-Boshrooyeh. Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks. Mod. Chem. 2014, 2(2), 15-18. doi: 10.11648/j.mc.20140202.12

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    AMA Style

    Yahya Hassanzadeh-Nazarabadi, S. Majed Modaresi, S. Bahram Jafari, Sanaz Taheri-Boshrooyeh. Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks. Mod Chem. 2014;2(2):15-18. doi: 10.11648/j.mc.20140202.12

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  • @article{10.11648/j.mc.20140202.12,
      author = {Yahya Hassanzadeh-Nazarabadi and S. Majed Modaresi and S. Bahram Jafari and Sanaz Taheri-Boshrooyeh},
      title = {Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks},
      journal = {Modern Chemistry},
      volume = {2},
      number = {2},
      pages = {15-18},
      doi = {10.11648/j.mc.20140202.12},
      url = {https://doi.org/10.11648/j.mc.20140202.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mc.20140202.12},
      abstract = {So far the methods used to predict or calculate the melting point of organic compunds do not focus on the compound nature, they mostly use microscopic physio-chemical properties of materials. In this paper the disadvantage of such traditional methods will be defined. Then a new method is introduced. This method uses the nature properties of compounds to estimate their melting point based on an artificial neural network and offsets the disadvantges of pervious ones.},
     year = {2014}
    }
    

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    AU  - Yahya Hassanzadeh-Nazarabadi
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    AU  - S. Bahram Jafari
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    N1  - https://doi.org/10.11648/j.mc.20140202.12
    DO  - 10.11648/j.mc.20140202.12
    T2  - Modern Chemistry
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    JO  - Modern Chemistry
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    UR  - https://doi.org/10.11648/j.mc.20140202.12
    AB  - So far the methods used to predict or calculate the melting point of organic compunds do not focus on the compound nature, they mostly use microscopic physio-chemical properties of materials. In this paper the disadvantage of such traditional methods will be defined. Then a new method is introduced. This method uses the nature properties of compounds to estimate their melting point based on an artificial neural network and offsets the disadvantges of pervious ones.
    VL  - 2
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Author Information
  • Mobile Robot Department, Parse Lab of Robotic, Mashhad, Iran

  • Chemistry Department, Ferdowsi University, Park Sq, Mashad, Iran

  • Chemistry Department, Tabriz Uneversity, Abresan, Tabriz, Iran

  • Mobile Robot Department, Parse Lab of Robotic, Mashhad, Iran

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