Support vector machine regression for reactivity parameters of vinyl monomers

• XINLIANG YU ^[2] ; XUEYE WANG ^[1] ; JIANFANG CHEN ^[2]
  1. [1] Xiangtan University

     Xiangtan University

     China

     
  2. [2] College of Chemistry and Chemical Engineering Hunan Institute of Engineering
• Localización: Journal of the Chilean Chemical Society (Boletín de la Sociedad Chilena de Química), ISSN-e 0717-6309, ISSN 0366-1644, Vol. 56, Nº. 3, 2011, págs. 746-751
• Idioma: inglés
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• Resumen

  • 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.