Prediction of Supercritical Carbon Dioxide Solubility of Organic Compounds from Molecular Structure

Abstract

A diverse data set of 58 compounds taken from the literature was used to create models for the prediction of the solubility of organic compounds in supercritical carbon dioxide. Descriptors encoding information about the topological, geometric, and electronic properties of each compound in the data set were calculated from the molecular structures. A multiple linear regression model containing seven descriptors was generated. Several new descriptors, which were not present in the original pool, were calculated. One of the new descriptors was used to create the final seven descriptor linear model, which had a better root mean square (rms) error than the original model. The seven descriptors that appeared in the final model were used to make a neural network model which had a significantly better rms error than the linear model.