The Clean Energy Project researchers have published a paper in the journal Energy & Environmental Science, describing their approach for discovering new materials for use in solar cells.
"Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics"
Lay Person Abstract:
The paper describes on-going work on discovering new solar cell materials and the approach taken by the Clean Energy Project from Harvard. The power produced by a solar cell depends on the product of voltage and current the cell supply. Both high voltage simultaneously with high current is most desirable to obtain the best solar cell efficiency. Certain material properties determine how much voltage can be produced and others determine the amount of current, often in conflict with each other. While the screening methods used are not always accurate, they seem to generally agree with experimental data that has shown certain materials to be most efficient. Their predictions point to certain classes of compounds most likely to lead to promising materials. Further work is being done to improve the screening process.
In this perspective we explore the use of strategies from drug discovery, pattern recognition, and machine learning in the context of computational materials science. We focus our discussion on the development of donor materials for organic photovoltaics by means of a cheminformatics approach. These methods enable the development of models based on molecular descriptors that can be correlated to the important characteristics of the materials. Particularly, we formulate empirical models, parameterized using a training set of donor polymers with available experimental data, for the important current–voltage and efficiency characteristics of candidate molecules. The descriptors are readily computed which allows us to rapidly assess key quantities related to the performance of organic photovoltaics for many candidate molecules. As part of the Harvard Clean Energy Project, we use this approach to quickly obtain an initial ranking of its molecular library with 2.6 million candidate compounds. Our method reveals molecular motifs of particular interest, such as the benzothiadiazole and thienopyrrole moieties, which are present in the most promising set of molecules.
Access to Paper:
To view the paper in Energy & Environmental Science, please click here.
To view the paper at Harvard University, please click here.