Machine learning (ML) has emerged as a powerful tool for developing interatomic models (IAM) capable of bridging the computational efficiency of classical molecular mechanics approaches and the predictive power for first-principles-based methods. In essence, this is achieved by directly learning the target potential energy surface (PES) topography onto a flexible set of basis functions, rather than trying to reconstruct the PES through analytical expressions as is typically done in classical "force field' strategies. This model generation approach is particularly useful for simulating complex systems (e.g., condensed phase reacting systems and materials under extremely high temperature and pressure conditions) for which a suitable molecular mechanics descriptions are not known a priori.