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Issue 1, 2005
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Estimation of melting points of pyridinium bromide ionic liquids with decision trees and neural networks

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Abstract

Regression trees were built with an initial pool of 1085 molecular descriptors calculated by DRAGON software for 126 pyridinium bromides, to predict the melting point. A single tree was derived with 9 nodes distributed over 5 levels in less than 2 min showing very good correlation between the estimated and experimental values (R2 = 0.933, RMS = 12.61 °C). A number n of new trees were grown sequentially, without the descriptors selected by previous trees, and combination of predictions from the n trees (ensemble of trees) resulted in higher accuracy. A 3-fold cross-validation with the optimum number of trees (n = 4) yielded an R2 value of 0.822. A counterpropagation neural network was trained with the variables selected by the first tree, and reasonable results were achieved (R2 = 0.748). In a test set of 9 new pyridinium bromides, all the low melting point cases were successfully identified.

Graphical abstract: Estimation of melting points of pyridinium bromide ionic liquids with decision trees and neural networks

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Publication details

The article was received on 14 Jun 2004, accepted on 25 Oct 2004 and first published on 03 Dec 2004


Article type: Paper
DOI: 10.1039/B408967G
Citation: Green Chem., 2005,7, 20-27
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    Estimation of melting points of pyridinium bromide ionic liquids with decision trees and neural networks

    G. Carrera and J. Aires-de-Sousa, Green Chem., 2005, 7, 20
    DOI: 10.1039/B408967G

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