Machine learning methodologies for accurate electron correlation energies and potential energy surfaces.

Studies on quantum systems impose a challenge for theoretical studies due to the compromise between accuracy and computational cost in their calculations. Machine learning methods are an approach to solve this trade-off by leveraging large data sets to train on highly accurate calculations using small molecules and then apply them to larger systems. In this study, we will discuss two machine learning projects: (1) the prediction of electron correlation energies and (2) neural network potentials for chemical reactions. To accurately predict the total correlation energy, we explore different machine learning architecture and features and discuss various trade-offs between complexity and performance. For neural network potentials, we discuss an active learning algorithm which allows for the accurate description of potential energy surfaces. Together, we believe these algorithms will allow for the accurate study of quantum devices.