Correlating parameters with molecular structure features for the ultimate goal of predicting these parameters has proven effective for thermodynamic parameters , gas-phase reaction rates , and transition state geometries . If shown that the properties of reacting molecules and solvent systematically affect reaction rates in liquids, such an approach can also be used to predict kinetic solvent effect. Machine learning provides a framework for parameter prediction based on molecular properties, using models fitted to known data. In addition to the above examples of chemical properties needed for detailed kinetic modeling, machine learning has been used to solve several problems in the domains of cheminformatics and bioinformatics .