The functionalities and performances of advanced materials and novel devices rely on the ability to control processes at the molecular level. Such control, in turn, hinges upon our understanding of the detailed mechanisms involving each molecular player, the interactions among them, and their constituent atoms. Indeed, a deep comprehension of molecular structures and supra-molecular interactions is decisive for the design and development of a plethora of advanced materials, as for instance dyes sensitized solar cells (DSSC) and photovoltaic devices, functionalized smart polymers, photonic liquid crystals or transition metal based technological applications. In the rational design of such materials, these requirements pose a significant challenge to computational techniques, which must achieve atomistic/molecular-level accuracy -often only attainable through quantum mechanical (QM) methods-while simultaneously accounting for a sufficiently large portion of the simulated material and providing a long enough observation time. In this framework, computational techniques based on simplified descriptions of the potential created by electrons, namely Molecular Mechanics (MM), along with methods of classical (CL) physics, such as Monte Carlo (MC) and Molecular Dynamics (MD), serve as a statistical tool for bridging such length/time scale gap. These techniques can be employed to gather reliable ensembles of (supra-)molecular configurations, which in turn can be used for successive QM calculations. In such sequential CL/QM methods, thermalized statistical ensembles of MC/MD configurations of large and complex systems are exploited in subsequent accurate QM calculations, allowing for an accurate characterization of material properties which intrinsically require a quantum treatment, as for instance spectroscopic features. The key ingredient of any CL simulation method is the force-field (FF), a collection of analytical model functions designed to represent the potential energy of the target system as a function of the nuclei's coordinates, where the chemical identity of each system component is entailed in the parameters specifying the model functions. The FF should clearly be capable of faithfully and accurately describing such a potential as best as possible. As a consequence, notwithstanding their computational convenience, general CL/QM multi-level approaches have two main drawbacks: i) the so-called "structural mismatch", which stems from a description of the potential energy surface (PES) delivered by available general-purpose FF parameters that is significantly different from the one provided by the subsequent QM approach; and ii) the unavailability of FF parameters for specific components of the simulated material. To overcome these drawbacks, the possibility to derive from accurate QM calculations FF parameters suitable for simple CL simulations has been explored for small molecules since the late 1970's. Nonetheless, their application to more complex systems has attracted a renewed interest only more recently. In fact, such an approach can in principle solve the two aforementioned drawbacks of general-purpose FFs based on transferability. On the one hand, tailoring the FF model on specific and accurate descriptors purposely obtained at QM level not only may allow for overcoming the structural mismatch, but also enforces the bridge connecting the CL and QM scales in subsequent analysis. On the other hand, modern electronic structure methods allow for the accurate representation of the structure and dynamics of molecules containing up to hundreds of atoms, thus paving the way for the FF parameterization of novel species or excited states.