Machine learning interatomic potentials (MLIPs) have emerged as a powerful tool for computational materials science and chemistry, bridging the gap between the accuracy of ab initio methods and the efficiency of classical force fields. This paper provides an overview of the key components, methodologies, and applications of MLIPs. The representation of atomic environments, the selection of appropriate machine learning models, the training process, and the prediction of energies and forces are discussed in detail. The manuscript explores the strengths and limitations of kernel-based, neural network, and graph-based MLIP approaches, highlighting their unique capabilities. Furthermore, the integration of MLIPs into molecular dynamics (MD) simulations, their role in accelerating materials discovery and design, and the motivation for the multi-atomic cluster expansion (MACE) model are presented. The application of the MACE potential to simple molecules, such as ethane and ethylene, demonstrates its ability to accurately capture the potential energy surface (PES). This work serves as a valuable resource for researchers interested in the development and application of MLIPs for a wide range of natural systems.