At а time when oil, coal and fossil gas are depleting and our air, water and soil are irreparably polluted, we need more than ever green alternatives to produce energy. The Sun is the best source of renewable energy which the mankind is striving to utilize it in the past 70 years. The organic solar cells possess unique advantages over the widely used silicon solar cells: they are flexible, low-cost, light weight and environment-friendly. Therefore, during the last few decades the research progress in the field of organic photovoltaic materials was very intense. Despite the significant amount of work that has been done in the field, the main drawback of organic solar cells remains their low power conversion efficiency.

Recently, it has been demonstrated that а successful strategy to improve the productivity of organic solar cells is the use of materials that undergo singlet fission (SF). In the conventional organic photovoltaics one photon of irradiation creates 2 charge carriers – one positive and one negative. The excited SF molecule is able to ‘share’ the excitation with an adjacent one and thus one photon creates 4 charge carriers – 2 positive and 2 negative. Accordingly, the efficiency is doubled but for the purpose a number of demanding conditions have to be met and the modelling of SF chromophores is a challenging non-trivial task.

To date, the known compounds satisfying all SF conditions are a small quota of the identified molecules. Therefore, the quest for new SF materials resembles the search of a needle in a haystack. The Project aims at finding much more than one needle but also to establish what makes these needles so special in order to suggest a strategy for their fabrication and fine tuning of their properties for the organic photovoltaics needs.

The experimental methods for the hunt of SF molecules are quite resource and time consuming, because they include intricate synthetic procedures and complex analytical techniques for sample characterization. Computational chemistry can offer a solution, as it can calculate molecular properties much faster and cheaper. If we order the SF requirements hierarchically, by means of Computational chemistry methods we can screen a huge number of organic chromophores to sort out those that meet the least demanding condition; subsequently, this selection can be filtered with respect to the next condition and so on until all requirements are satisfied and new potential materials are identified. The execution of this strategy requires a multidisciplinary approach: beside the quantum-mechanical methods, statistical data analysis techniques are mandatory. Highly suitable for the Project’s goals are the machine learning (ML) algorithms which gained substantial popularity lately, since they allow the processing of enormous data sets for sifting the desired entries or for predicting the missing ones. In the framework of the Project a database of molecules with SF potential for direct use in solar cells or to be utilized as building blocks for new photovoltaic materials manufacturing will be created. On the other hand, in the process of ‘sifting’ relationships between molecular structure and SF capacity will be extracted to serve for the design of new molecules for high-efficiency low-cost organic photovoltaics. Experimental testing of the most promising SF candidates will be undertaken.