ML-IAM development is typically accomplished through five high-level steps: (1) initial training data are generated, comprising a series of system configurations with corresponding forces, energies, and/or stresses assigned via, e.g., Kohn-Sham Density Functional Theory 1 (DFT); (2) system configurations are then recast as a series of "descriptors" describing the local chemical environments; (3) a model architecture is selected, e.g., a smooth set of basis functions that ingest these descriptors and can be used to predict corresponding energies and by consequence, forces, and stresses; (4) model parameters are determined via optimization;