In recent years, machine learning interatomic potentials (MLIPs) have emerged as successful tools for accurately approximating ab initio potential energy surfaces at a significantly reduced computational cost: roughly milliseconds instead of minutes per single-point evaluation of a typical structure found in this work. MLIPs have found applications in various computational chemistry problems, ranging from highly accurate spectra prediction long molecular dynamics (MD) simulations of electrolytes, path-integral MD of supramolecular complexes, to crystal structure prediction, heterogeneous catalysis and radical reaction networks. MLIPs offer an efficiency advantage by avoiding the need to solve the all-electron Schrödinger equation; instead, they predict energy and forces based directly on atom positions. They are constructed based on local environment representations, enabling linear scaling and transferability to structures orders of magnitude larger than those in the training set. Unlike empirical force fields, MLIPs do not rely on a predefined functional form and exhibit flexibility in representing complex potential energy surfaces.