It is important to underscore that other researchers have previously thought about some of the issues outlined above, and several potential (partial) solutions have been proposed over the years. One strategy that has been explored is the use of a recommender system, i.e., a machine-learning (ML) model that will predict which F xc ought to perform best for the specific (molecular) system under consideration. Typically, a metric, indicative of the accuracy of the F xc is designed, and then a multi-task regression model is trained. 17,18,21 Upon inference, the predicted scores of the individual functionals are ranked, and the model will recommend the best one. While arguably the most robust approach to deal with variations in functional performance across chemical space, it is important to appreciate the extreme computational cost of generating reliable training datasets for such recommender models.