To address the challenge of studying realistic systems with high accuracy and reasonable computational cost, emerging techniques such as machine learning (ML) have gained widespread adoption in the materials science community in the last ten years. ML-based interatomic potentials (MLIPs) offer the potential to achieve a desired level of accuracy at orders of magnitude lower computational cost compared to ab initio molecular dynamics (AIMD). One key difference between MLIPs and FFs is the functional form used to represent the interaction potential. While FFs have a fixed functional form, MLIPs do not make assumptions about the form of the potential, except for some physical constraints such as smoothness of the function and a cutoff distance for interactions. This flexible functional form allows MLIPs to achieve the accuracy of the calculations used for training within an arbitrary error. However, the evaluation of forces from MLIPs can be more computationally expensive than FFs due to the complexity of the model, but this cost is still orders of magnitude lower than ab initio calculations. In addition, the computational cost of MLIPs (which depends on the ML model) generally scales linearly with system size, in contrast to the cubic or higher-order scaling of DFT methods.