Support Vector Machine Regression for Reactivity Parameters of Vinyl Monomers

Abstract

Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, ε-support vector regression (ε-SVR) and ν-support vector regression (ν-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 CH 3 CH 2 C 1The optimum ε-SVR model of lnQ (C= 9, ε =0.05 and γ =0.2) and the optimum ν-SVR model of e (C=100, ν = 0.5 and γ =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.

Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth

“…Closer proximity to the core of the protected area affords a much higher level of protection as compared to buffer zones; core areas are thus subject to lower rates of forest loss and resource extraction. Another major cause of AGB loss that is apparent not only in Angkor Thom, but also in other parts of tropical Asia [32] is the proximity of an area to various sources of anthropogenic disturbance, such as roads.…”

“…The value of ϕ(x) is not known, and kernel functions are used for mapping the predictor variables into higher dimensions [32]. Radial basis function (RBF) is a kernel function that is commonly used to account for such data and which produces robust results compared to other kernels [33,34].…”

Prediction of the Q-e parameters from structures of transfer chain agents

“…The QSPRs analysis can be a tool to estimate reactivity of monomers by means of the analysis of their molecular structures [18][19][20].…”

Prediction of Monomer Reactivity in Radical Copolymerizations from Transition State Quantum Chemical Descriptors

“…APT charge on an atom is related to trace of the corresponding tensor of derivatives of dipole moment with respect to Cartesian coordinates of that atom [17] . Support vector machine (SVM) is a set of learning algorithm mainly used to resolve the classification and regression problem [8,9,[18][19][20][21][22][23] . In SVM, systems use the input data into a high dimensional feature space and subsequently carry out the linear regression in the feature space.…”

“…Therefore Equation 1 is extremely useful in predicting and controlling the composition of any copolymer produced from any pair of monomers at any concentration ratios [1,2] . But Equation 1 may be limited because of the shortage of the values of r 12 and r 21 . The Q-e scheme can be used to estimate the monomer reactivity ratios with following equations [1][2][3] [ ] …”