Neural Network Prediction of Interfacial Tension at Crystal/Solution Interface
Interfacial tension at the crystal/liquid interface is a crucial and important parameter in crystal growth kinetics. The objective of the present study is to develop a neural network that is simple to use for predicting this important parameter using only from the information of solubility, molecular weight, and density of the studied systems. A three-layer feed-forward neural network was constructed and tested to predict the interfacial tension at the crystal/solution interface. The concentration of solute in liquid phase, concentration of solute in solid phase, temperature, density and molecular weight of crystal were used as inputs to predict the interfacial tension at the crystal/liquid interface (σSL). The network was trained using the solubility information for 28 systems to predict the σSL value and was validated with 29 new systems. Despite the limited number of data used for training, the neural network was capable of predicting σSL successfully for the new inputs, which are kept unaware during the training process. The σSL value that is predicted by the artificial neural network during the training and testing process was compared with σSL predicted from the widely used empirical expression. For most of the systems, ANN better predicts σSL, when compared to empirical correlation.