The computational cost of black-box potentials currently lies between those of ab initio and classical force fields. This does not include the computational cost of training the black box, or creating the QM training data set, the size of which depends on the ML approach of choice. The cost associated with training data can be alleviated by exploiting or creating curated, open-access databanks and by using strategies, such as active learning, to sample the training data efficiently. (2) Gray-box potentials. There are two standard strategies for parameterizing a classical force field functions for MD simulations, either by 1) computing the function parameters from highly accurate QM calculations for (as large as possible) molecules and molecular fragments and piecing the model together bottom-up; or 2) optimizing the function parameters for molecules or multi-molecule systems directly against target experimental data.