SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS
Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, å- support vector regression (å-SVR) and v-support vector regression (v-SVR) were, respectively, used to construct quantitative structure-property relations...
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| Autores principales: | , , |
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| Lenguaje: | English |
| Publicado: |
Sociedad Chilena de Química
2011
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072011000300006 |
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| Sumario: | Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, å- support vector regression (å-SVR) and v-support vector regression (v-SVR) were, respectively, used to construct quantitative structure-property relationship (QSPR) models of Q and e parameters in the Q-e scheme, which is remarkably useful in the interpretation of the reactivity of a monomer in free-radical copolymerizations. The quantum chemical descriptors used to developed the SVR models were calculated from styrene and radicals with structures CH3CH2C¹H2-C²HR³· (C¹H2=C²HR³ + CH3CH2· - CH3CH2C¹H2-C²HR³·). The optimum å-SVR model of lnQ (C= 9, å =0.05 and ã =0.2) and the optimum v-SVR model of e (C=100, v = 0.5 and ã =0.4) produced low root mean square (rms) errors for prediction sets: 0.318 and 0.266, respectively. Thus, applying SVR to predict parameters Q and e is successful. |
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