Developing generalizable machine learning models using electronic structure-based features

Machine learning has been widely applied to predict molecular properties (i.e., total energy), by utilizing the patterns and relationships between a system's features and the desired property. Developing generalizable machine learning models capable of making accurate predictions on data not included in the training process requires features with a clear and meaningful connection to the desired molecular property. Recently, features that incorporate information beyond molecular geometry, to include a system's underlying physics, have been demonstrated to result in highly generalizable machine-learning models. In this study, we explore the construction of a feature space that includes the underlying physics of a system by using information obtained from computationally affordable electronic structure calculations, such as Hartree-Fock. We then assess these electronic structure-based features by training a neural network to predict ab initio (i.e., CCSD) formation energies. This research aims to determine if these types of features result in machine learning models that are both accurate and generalizable.