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

doi:10.1088/1674-1056/23/4/046101

中国物理B ›› 2014, Vol. 23 ›› Issue (4): 46101-046101.doi: 10.1088/1674-1056/23/4/046101

• CONDENSED MATTER: STRUCTURAL, MECHANICAL, AND THERMAL PROPERTIES • 上一篇下一篇

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

Amani Tahat^a, Jordi Marti^a, Ali Khwaldeh^b, Kaher Tahat^c

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

收稿日期:2013-06-23 修回日期:2013-10-03 出版日期:2014-04-15 发布日期:2014-04-15

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

Amani Tahat^a, Jordi Marti^a, Ali Khwaldeh^b, Kaher Tahat^c

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

Received:2013-06-23 Revised:2013-10-03 Online:2014-04-15 Published:2014-04-15
Contact: Amani Tahat E-mail:amanitahat@yahoo.com
About author:61.20.Qg; 31.15.xv; 07.05.Mh

摘要/Abstract

摘要： 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.

关键词: pattern recognition, proton transfer, chart pattern, data mining, artificial neural network, empirical valence bond

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.

Key words: pattern recognition, proton transfer, chart pattern, data mining, artificial neural network, empirical valence bond

中图分类号: (Structure of associated liquids: electrolytes, molten salts, etc.)

61.20.Qg

31.15.xv (Molecular dynamics and other numerical methods) 07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)

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[J]. 中国物理B, 2014, 23(4): 46101-046101.

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

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

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