DMREF: High-Throughput Morphology Prediction for Organic Solar Cells

DMREF：一个高效率的计算形态预测有机光伏电池鲍哲南（斯坦福大学），Vijay Pande（斯坦福大学），Michael Toney（SSRL）非技术说明：有机光伏电池（OPV）是传统太阳能电池的替代品，因为它们承诺低成本的大规模生产结合轻量级和灵活的应用。特别是，它们提供了一个前景，为欠发达国家农村地区无法接入电网的数百万人提供基本电力。文献中报道了许多OPV材料，但很少有材料显示出大于8%的效率。关键的挑战是设计满足所有要求的材料。典型的OPV由混合在一起的供体和受体组成。预测纳米级形态仍然是预测OPV性能的最大挑战之一。因此，许多材料组合和大的工艺参数空间（如施主/受主比，溶剂，退火条件，薄膜厚度）目前需要screen.Technical描述：本项目旨在为OPV材料的高通量形态预测的综合研究计划。理论、合成和表征之间的持续反馈循环将促进结果的交流，并简化整个开发过程。该项目的一个中心主题是开发高通量的计算形态学计算和实验表征技术。计算开发利用了分布式志愿者计算网络提供的巨大计算能力。Pande将重组他的大规模Folding@home模拟引擎（最初是为生物分子的分子力学/动力学研究而开发的），以预测OPV的体异质结混合形态。Folding@home使Pande和同事能够执行以前无法执行的计算，使他们能够达到比传统方法长数千到数百万倍的时间尺度。鲍将设计合成路线，制备用于薄膜制备的模型化合物，并与理论预测的形态进行比较。Bao和Toney将一起对化合物进行光电，结构和形态测量。表征将建立和采用新的高通量仪器。实验数据，无论是积极的还是消极的结果，都将提供给理论小组，并将其添加到经验数据的集合中。后者用于校准方案，并提供了许多所采用的模型的参数化。扩展此数据集将改善相关的建模工作及其预测能力。这反过来将导致发展进程的调整。一个广泛的成果和参考数据库将作为三个参与小组之间信息交流的中心。该项目过程中积累的大量数据将为更好地理解分子结构/形态相关性提供基础，并将成为OPV社区的公开可用资源。

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.