Attempts have been made to connect quantum chemical calculations and force field expressions on a more fundamental level. For example, Bond Order Potentials (BOP) can be viewed as expressions closely related to the realm of density functional (or wave function based) calculations. Apart from physical clarity, these approaches avoid a proliferation of fitting parameters. However, despite their appealing nature, these approaches are not widely used, except for the Tight Binding (TB) method which essentially also falls in this category. Much more widely used are machine learning approaches. These methods assume that the total energy of a system is the sum of the energy of all atoms in their respective environments. As a first step, the cartesian coordinates of this environment are converted into symmetry-adapted descriptors. In the two most widely used approaches, these descriptors are inputs to a neural network or to a Gaussian Approximation Potential. The parameters of these modules are obtained by fitting to the results of DFT calculations. The main advantage of the machine-learning approach is the flexibility of the potential energy expression and its learning-onthe-fly potential.