Machine learning (ML) methods have been proposed as surrogates for time-consuming quantum mechanical calculations, such as density functional and first-principles methods, for their rapid prediction potential once trained. For ML to be a successful surrogate, the methods need to be able to perform property predictions adequately for optimized geometries, capture not just the well of the potential energy curve but also the anharmonicity that force field methods fail to capture, and appropriately handle multiple conformations of the same molecule.