New Chemometric Algorithms Based On Chaotic Concept And QSAR Aided Quantum Chemical Calculations

Quantitative Structure-Activity Relationship (QSAR) is one of the important branches in chemometrics. The artificial neural network, a powerful modeling method for the nonlinear statistical studies, has some drawbacks like the possible premature convergence to the local optima and overfitting to the samples in the training set. One of the motives of the use of genetic algorithm (GA) in an optimization procedure is to avoid sinking into local optima. Actually, only a few fittest members of the whole population of a generation can survive during GA's selection operation. After some generations the population diversity would be greatly reduced and the algorithm might lead to a premature convergence to a local optimum. To circumvent these shortcomings, the chaotic dynamic system is introduced in the studies of chemometric algorithms. The chaotic mapping itself is very sensitive to the initial state in a manner similar to the statistical randomness but with its underlying patterns appearing to be phantasmagoric in a deterministic style. The characteristic feature of chaos itself being able to search the space of interest exhaustedly has been employed to improve the performance of chemometric algorithms studied. We hope the usage of chaotic concept will shed some light onto the algorithm studies. Although the chaotic mapping model can be directly used as an algorithm in training an ANN, the efficiency of such an approach seems to be insufficient. A chaotic system applied as mutation operation in GA could significantly enhances GA's potential in terms of maintaining the population diversity during the evolutionary process. This scheme effectively prevents the incest during the evolution of the general GA leading to misleading local optima. The effectiveness of such a chaotic concept based algorithm has been proved clearly and demonstrated in overcoming the convergence to local optima and also the overfitting to training samples often appearing in traditional neural network training. The effectiveness over tradational GA in training ANN of the proposed scheme have been demonstrated in predictions of the vibration frequencies of the tetrahedral tetrahalide species, of the vibration frequencies of the octahedral hexahalide species, of the fluorophilicity, and of the atmospheric lifetime of the substitutes of chlorofluorocarbans (CFCs) based on physico-chemical and semi-empirical quantum chemical parameters and density functional theory (DFT) calculations.The newly proposed prepotency evolution (PE) algorithm based on the concept of"prepotency" has a higher convergence speed comparing to the conventional genetic algorithm. The PE begins with an initial population distributing in the whole space of interest and converges with a high speed and does not jazz around local optima like the traditional genetic algorithms. This manner may result from its underlying direction selection for its evolution. To take advantage of the chaotic mapping, a repetition prepotency evolution algorithm with a population initialized by chaotic numbers is developed in which the chaotic mapping is an uncommon seeding-machine to produce different initial 'chromosome' populations never appeared before for PE algorithm. The combination of chaotic mapping and PE makes PECA be able to probe lots of optima rapidly and effectively owing to the quick convergence speed of PE and relatively high population diversity guaranteed by chaotic mapping. When this scheme is tested in ANN training to predict the vibration frequencies of tetrahedral tetrahalide species, the results show that it greatly enlarges the opportunity to find the global optimum. This scheme can also be introduced in many other optimization procedures and will possibly carry out better results.The QSAR study of catalytic activity of iron-tetraphenylporphyrin chloride (Fe(TPP)Cl) and its 7 halogenated complexes in the oxidation reaction of isobutene was performed based on quantum chemistry calculation. The electronic structural characteristics, and their relationship wit...