It should be noted that the generation of reference data at the DFT level and the training of the machine-learned potential are not necessarily done in two distinct steps, and there are advantages to more involved methodologies. Vandenhaute et al. demonstrated the possibility of an incremental learning scheme to construct accurate and data-efficient MLPs of MOFs by combining enhanced sampling and on-the-fly training, in a scheme illustrated on Figure . Wieser et al. highlighted the accuracy of MLPs developed by MD-based active learning for a variety of MOF properties, including elastic constants, phonon band structures, and thermal conductivity.