A Bottom-up Computational Approach to Semiconducting Block Copolymers

Conjugated polymers are very attractive materials for the scientists and industry due to low cost of the organic compounds, their lightweight, easy large-area processing from solution at low temperature and mechanical flexibility. Moreover, these materials are multifunctional and advanced technologies require both simultaneous n- and p-type conductance, i.e. ambipolarity. However, there are some hindrances which do not allow the wide spreading of this new generation of semiconductors into the market, first of all, due to their instability to ambient conditions. Moreover, determination of the tunable parameters which are responsible for high efficiency and controlled crystal packing ordering of the devices is rather complicated. A lot of efforts are done in order to improve the performance of the organic electronics as well as to shed light on the relation between the chemical structure and their intrinsic properties. Additionally, the governing factors which define the conductive properties of these materials are still under debate and this remains a great challenge for the researchers. One way to gain insight into the characteristics of polymeric materials is to begin exploring the polymers from their small constitutive units and then step-by-step to construct and characterize every compound up to macromolecular level. In this work, the semiconducting block copolymers, as promising candidates for application in organic transistors, are investigated starting from their small donor and acceptor blocks up to monomers and macromolecules, using computational methods running on different time and length scales. It is found out that the charge transport depends on the symmetry of molecules and the hopping mobilities can be predicted from isolated stacks of dimers, which are defined by minimum energy, without knowledge of the actual crystal structure. Interestingly, the polymers moieties prefer to build up mixed stacks and the flanks form segregated columns if there are no present defects in the samples. At each step of the investigation the results are compared with available experimental data