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Chin. Phys. B, 2014, Vol. 23(4): 046101    DOI: 10.1088/1674-1056/23/4/046101
CONDENSED MATTER: STRUCTURAL, MECHANICAL, AND THERMAL PROPERTIES Prev   Next  

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

Amani Tahata, Jordi Martia, Ali Khwaldehb, Kaher Tahatc
a Department of Physics and Nuclear Engineering, Technical University of Catalonia-Barcelona Tech, B4-B5,North Campus UPC, 08034 Barcelona, Catalonia, Spain;
b Department of Computer Engineering, Faculty of Engineering, Philadelphia University, Amman, Jordan;
c Department of Graduate Business Studies, Waterford Institute of Technology, Main Campus Cork Road, Waterford, Ireland
Abstract  In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer‘occurred’and transfer‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
Keywords:  pattern recognition      proton transfer      chart pattern      data mining      artificial neural network      empirical valence bond  
Received:  23 June 2013      Revised:  03 October 2013      Accepted manuscript online: 
PACS:  61.20.Qg (Structure of associated liquids: electrolytes, molten salts, etc.)  
  31.15.xv (Molecular dynamics and other numerical methods)  
  07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)  
Corresponding Authors:  Amani Tahat     E-mail:  amanitahat@yahoo.com
About author:  61.20.Qg; 31.15.xv; 07.05.Mh

Cite this article: 

Amani Tahat, Jordi Marti, Ali Khwaldeh, Kaher Tahat Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments 2014 Chin. Phys. B 23 046101

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