thermal and many other properties of disordered materials from their chemical composition, and some studies have started to implement process optimization, too . In most of these cases, however, single-response models are still being used (Figure ), in which a certain output parameter is obtained from a set of input features. On the other hand, complex material processing typically involves a multitude of intercorrelated reactions, leading to output trade-offs among a range of desirable feedback parameters. For this reason, some machine learning models have been designed at higher dimension, implementing multiple simultaneous output parameters , but still lacking the full breadth of possible output interdependencies. Instead, model input features are typically selected so that the regression model performance is improved, and insight is obtained into the impact of these input features on a particular, individual property .