Research and prediction of coordination reactions between CPA-mA and some metal ions using artificial neural networks

The complex relationship between maximum absorption wavelength (lambda(max)), molar absorptivity (epsilon) of the coordination compounds formed from m-acetyl-chlorophosphonazo (CPA-mA) and the metal ions, the acidity of coordination reaction, some properties of metal ions and the properties of more than 20 coordination compounds were studied using artificial neural networks with extended delta-bar-delta EDBD back learning algorithms in this paper. Six parameters: the pH of coordination reactions, metal ion radius (R), relative atomic weight (Wt), ionic electronic energy (E), metal ion standard Gibbs' free energy (deltaG0) and hard-soft acid-base dual scale (f) were used as input parameters, to predict the lambda(max) and epsilon of the coordination compounds. The structures of networks and the learning times were optimized. The best networks structure is 6-7-2. The optimum number of learning times is about 160,196. It is shown that the maximum relative error is no more than 6% in the testing set. The trained networks are used to simulate the complicated relations between the metal ion properties, coordination reaction conditions and the properties of coordination compounds. This optimized networks have been used for the prediction of the lembda(max) and epsilon of coordination compounds formed from Tb3+, Ho3+ with CPA-mA separately and with satisfactory results.