For a few years now, quantum chemical modeling of materials has experienced a tremendous boost due to the increasing computational power. However, regardless of whether Moore’s law is respected or not, the difficulty of modeling has now shifted to the construction of the model itself. Of course, the accuracy of the calculations can still be improved, but the main chemical properties and their trends are relatively well reproduced today, especially when they are combined with experiments. One can say that density functional theory (DFT) is now at a mature age and that it can be used as a reliable prediction tool in material science applications, although some work can be done on accuracy. Nevertheless, DFT is especially efficient in describing chemical phenomena at the molecular level, whereby the studied systems increase continuously in size and complexity. Indeed, the size of the system is directly related to the computation power, and the complexity is related to the quality of the calculation method and the representation of the chemical environment in the model. It is the latter property that brings the computational chemist’s chemical intuition and general chemistry knowledge at the forefront. In this Special Issue, we wanted to focus on the construction of pertinent models that are able to describe and predict, as accurately as possible with the available computational power, the chemistry of materials.