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

The complex relationship between maximum absorption wavelength (λ_max), molar absorptivity (ε) 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 (ΔG⁰) and hard–soft acid–base dual scale (f) were used as input parameters, to predict the λ_max and ε 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 λ_max and ε of coordination compounds formed from Tb³⁺, Ho³⁺ with CPA-mA separately and with satisfactory results.