Study of Nematic Transition Temperatures in Themotropic Liquid Crystal Using Heuristic Method and Radial Basis Function Neural Networks and Support Vector Machine

Quantitative Structure-Property Relationships (QSPRs) models have been successfully developed for the prediction of the nematic transition temperatures (T(N)) of 42 thermotropic liquid crystals. Heuristic Method (HM) and Radial Basis Function Neural Networks (RBFNNs) and Support Vector Machine (SVM) were utilized to Construct the linear and non-linear QSPRs models. respectively. Comparing the whole results obtained front the three models, the RBFNNs model was much better. The optimal QSPRs model which was established based on RBFNNs gave a square correlation coefficient (R(2)) of 0.984, 0.953, 0.973, and Root-Mean Square (RMS) error of 2.19, 4.13, and 2.99 for the training set, the test set, and the whole set, respectively. Some analysis to the dataset and evaluation were done in the paper. All the results indicated that the proposed QSPRs model was robust and satisfactory.