Exploring structure-property relationships in chemical space through quantum mechanical and machine learning methods

This thesis explores the intersection of machine learning algorithms and quantum chemistry to evaluate structure-property relationships in molecules. This work developed new methods for prediction of molecular properties, leveraging the speed of statistical regression models with the accuracy of quantum chemical calculations.