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Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments
dc.contributor.author | Tahat, Amani |
dc.contributor.author | Martí Rabassa, Jordi |
dc.contributor.author | Khwaldeh, Ali |
dc.contributor.author | Tahat, Kaher |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Física i Enginyeria Nuclear |
dc.date.accessioned | 2014-02-27T14:47:41Z |
dc.date.created | 2014-02-10 |
dc.date.issued | 2014-02-10 |
dc.identifier.citation | Tahat, A. [et al.]. Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments. "Chinese Physics B", 10 Febrer 2014, vol. 23, núm. 4, p. 046101-1-046101-12. |
dc.identifier.issn | 1674-1056 |
dc.identifier.uri | http://hdl.handle.net/2117/21794 |
dc.description.abstract | In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing to classify 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 of 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. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Física |
dc.subject.lcsh | Pattern perception |
dc.subject.lcsh | Data mining |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.other | proton transfer |
dc.subject.other | pattern recognition |
dc.subject.other | neural networks |
dc.subject.other | water environments |
dc.subject.other | chart pattern |
dc.subject.other | data mining |
dc.subject.other | artificial neural network |
dc.subject.other | empirical valence bond |
dc.title | Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments |
dc.type | Article |
dc.subject.lemac | Reconeixement de formes (Informàtica) |
dc.subject.lemac | Mineria de dades |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. SIMCON - First-principles approaches to condensed matter physics: quantum effects and complexity |
dc.identifier.doi | 10.1088/1674-1056/23/4/046101 |
dc.relation.publisherversion | http://cpb.iphy.ac.cn/EN/abstract/abstract58541.shtml |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 13065421 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Tahat, A.; Marti, J.; Khwaldeh, A.; Tahat, K. |
local.citation.publicationName | Chinese Physics B |
local.citation.volume | 23 |
local.citation.number | 4 |
local.citation.startingPage | 046101-1 |
local.citation.endingPage | 046101-12 |
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