Numerous studies have shown the proficiency of ML methods to predict thermochemical parameters at already optimized geometries utilizing various types of representations and neural network structures. Early representations, such as Coulomb Matrix and bag-offeatures, demonstrated success in property predictions with further iterations of representations such as FCHL continuing to improve the property prediction at optimized geometries. These ML methods are typically trained on the QM7 or QM9 data sets consisting of optimized molecules with up to 7 or 9 heavy atoms respectively and help to demonstrate ML's potential as a surrogate.