Machine learning methods have had a significant impact across many domains, not least in chemistry. New methods for the analysis and simulation of molecules and materials have emerged based on developments in machine learning. For example, machine learning has been used to design new materials, to predict protein structure, 35 ground 36 and excited state molecular properties, and even aid in the interpretation of complex molecular dynamics trajectories. For the selected CI problem, machine learning has been used to predict important configurations using supervised learning on data generated on-the-fly. This enables more accurate potential energy surfaces with fewer iterations as compared to a stochastic sCI approach. Most applications of ML for quantum chemistry are in either the domain of supervised or unsupervised learning and require large amounts of data. The quality of the application depends on the amount and type of data, and even then the training may not generalize to previously unseen scenarios, making (un-)supervised learning difficult to apply to broad problem classes in general.