The Harvard Clean Energy Project (http://cleanenergy.harvard.edu) is a computational materials science project designed to help find high-performance organic photovoltaics (OPVs). It represents an effort to unite quantum chemistry with techniques from virtual high-throughput screening, materials informatics, and data-driven materials design. Our in silico research is tightly embedded into the efforts of experimentalist partners.Some of the prerequisites for efficient OPVs are known, but engineering materials, which combine all these features is a hard problem. Experimental developments are predominantly based on empirical intuition, professional inspiration, or experience with certain compound families. The work is labor- and resource intensive and progress tends to be incremental. Theory is ideally suited to provide guidance, qualitative insights, and candidate rankings. Traditional computational modeling is, however, generally also restricted to small sets of candidates, for which a few selected aspects of the PV process are studied. While these individual approaches are valuable, they severely limit the explored chemical space and the conclusiveness of the results. Yet the vast structural possibilities of organic compounds may well hold the key to overcoming the deficiencies prevalent in today's OPV materials. If we are able to sample a broader range of candidates, radically different compound classes may emerge and open the door to the next generation of carbon-based solar cells.We have established an automated high-throughput infrastructure that facilitates the computational screening of millions of molecular candidates at various levels of theory. Our current efforts focus on the analysis and mining of the massive volume of data generated by our research, employing tools from materials informatics, machine learning, and pattern recognition. We believe that this comprehensive approach will help advance the utility of computational materials science and accelerate the development of novel materials.

High-throughput and big data techniques in computational materials science

246th National Meeting (2013)

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