Exploratory, Regression, and Neural Network Analysis of the Stability of Cation Coronates in Selected Pure Solvents

Abstract

Exploratory, regression, and neural network analysis of the stability constants of crown ether [12C4, 16C5, (CH3)216C5, DB21C7, DB24C8, DCH24C8, DB30C10] 1 : 1 complexes with alkaline (Li+, Na+, K+, Cs+, Rb+), alkaline-earth (Ca2+, Sr2+, Ba2+), and heavy (Ag+, Tl+, Co2+, Cu2+, Pb2+) metals and NH4+ in water and organic solvents (methanol, acetonitrile, acetone, N,N-dimethylformamide, nitrobenzene, nitromethane, 1,2-dichloroethane, propylene carbonate) at 298.15 K obtained via conductometry has been performed. Factor, cluster, discriminant, canonical, decision tree, regression, and neural network models of clustering, approximation, and prediction of thermodynamic constants of the complexation depending on the properties of the ligand, the cation, and the solvent have been developed. The trained MLP 7-5-5 Multilayer Perceptron Cluster has completely confirmed the k-means clustering. Independent data on the stability constants of coronates have demonstrated the predictive capacity of the trained perceptron-approximator MLP 7-7-1.