DMREF: High-Throughput Morphology Prediction for Organic Solar Cells

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DMREF: A High-Throughput Computational Morphology Prediction for Organic PhotovoltaicsZhenan Bao (Stanford), Vijay Pande (Stanford), Michael Toney (SSRL)Non-Technical Description: Organic photovoltaic cells (OPVs) are alternatives to conventional solar cells as they promise low-cost mass production combined with lightweight and flexible applications. In particular, they offer a prospect to provide basic electricity to the millions of people in rural areas of undeveloped countries who lack access to the power grid. Many OPV materials have been reported in the literature, but few have shown efficiencies greater than 8%. The key challenge is to design materials that fulfill all the requirements. A typical OPV consists of a donor and an acceptor blended together. Predicting the nanoscale morphology remains one of the biggest challenge in predicting OPV performance. Therefore, many material combinations and large processing parameter space (e.g. donor/acceptor ratio, solvents, annealing conditions, film thickness) presently need to be screened.Technical Description: This project aims at an integrated research plan for the high throughput morphology prediction of OPV materials. A continuous feedback loop between theory, synthesis and characterization will facilitate the exchange of results and streamline the overall development process. A central theme of this project is to develop high-throughput techniques for computational morphology calculation and experimental characterization. The computational development takes advantage of the massive computing power provided by distributed volunteer computing networks. Pande will retool his massive Folding@home simulation engine (which was originally developed for molecular mechanics/dynamics research on biomolecules) to predict the bulk-heterojunction blend morphology for OPVs. Folding@home has allowed Pande and coworkers to perform calculations that could not be performed before, by allowing them to reach timescales that are thousands to millions of times longer than would be possible by traditional means. Bao will design synthesis routes to prepare model compounds for thin film preparation and comparison with theoretically predicted morphology. Bao and Toney will together perform optoelectronic, structural, and morphological measurements on the compounds. The characterization will establish and employ new high-throughput instrumentation. The experimental data, regardless of positive or negative outcome, will be made available to the theory group where it will be added to a collection of empirical data. The latter is utilized in calibration schemes and provides the parameterization for many of the employed models. Extending this data set will improve the related modeling efforts and their predictive capacity. This in turn will lead to an adjustment of the development processes. An extensive results and reference database will serve as the hub for the information exchange between the three participating groups. The vast amount of data accumulated in the course of this project will provide the foundation for a better understanding of the molecular structure/morphology correlations, and it will be an openly available resource for the OPV community.