There are two ways to evaluate the properties of unknown chemical compounds. One is by traditional approaches, which measure the desired data from the experiments and the other is by predicting them in the theoretical approaches using a kind of prediction model. The latter are considered to be more effective because they are less time consuming and cost efficient, and there is less risk in conducting the experiments. Besides, it is inconvenient to conduct experiments to obtain experimental data, especially for new materials or high molecular substances. Several methods using regression model and neural network for predicting the physical properties have been suggested so far. However, the existing methods have many problems in terms of accuracy and applicability. Therefore, an improved method for predicting the properties is needed. A new method for predicting the physical property was proposed to predict 15 physical properties for the chemicals which consist of C, H, N, O, S and Halogens. This method was based on the group contribution method that was oriented from the assumption that each fragment of a molecule contributes a certain amount to the value of its physical property. In order to improve the accuracy of the prediction of the physical properties and the applicability, we extended the database, significantly modifying the existing group contribution methods, and then established a new method for predicting the physical properties using support vector machine (SVM) which is a statistical theory that has never been used for predicting the physical properties. The SVM-based approach can develop nonlinear structure property correlations more accurately and easily in comparison with other conventional approaches. The results from the new estimation method are found to be more reliable, accurate and applicable. The newly proposed method can play a crucial role in the estimation of new compounds in terms of the expense and time.

Group Contribution Method Functional Group Support Vector Machine Property Estimation

Mannan MS, Rogers WJ, Aldeeb A, A systematic approach to reactive chemicals analysis, Proc. HAZARDS XVI, Manchester, U.K. 41-58 (2001)
Bruneton C, Hoff C, Barton P, Computers and Chemical Engineering, 22(6), 735 (1998)
Joback KG, Unified approach to physical property estimation using multivariate statistical techniques, S.M. Thesis, Massachusetts Institute of Technology, Cambridge (1984)
Joback KG, Designing molecules possessing desired physical property values Vol.1, Ph. D. Thesis, Massachusetts Institute of Technology, Cambridge (1989)
Joback KG, Designing molecules possessing desired physical property values Vol. 2, Ph. D. Thesis, Massachusetts Institute of Technology, Cambridge (1989)
Joback KG, Fluid Phase Equilib., 185(1-2), 45 (2001)
Liaw HJ, Yur CC, Lin YF, J. of Loss Prevention in the Process Industries, 13(6), 499 (2000)
Constantinou L, Gani R, AIChE J., 40(10), 1697 (1994)
Lee KH, Jung JY, Lee IB, HWAHAK KONGHAK, 31(6), 744 (1993)
DIPPR (Design Institute for Physical Properties), http://dippr.byu.edu
Vapnik VN, The nature of statistical learning theory, Springer Verlag, New York, U.S.A. 53-67 (1995)
Cherkassky V, Muler F, Learning from data: Concepts, theory, and methods, John Wiley & Sons, New York, U.S.A. 353-387 (1998)
Liaw HJ, Chen CJ, Yur CC, J. of Loss Prevention in the Process Industries, 14(5), 371 (2001)