DMREF: High-Throughput Morphology Prediction for Organic Solar Cells

DMREF：有机太阳能电池的高通量形态预测

• 批准号: 1434799
• 负责人: Zhenan Bao
• 金额: $ 90万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1434799&HistoricalAwards=false
• 关键词: DMREF; Throughput; Morphology; Prediction; Organic

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.

DMREF：一种用于有机光伏的高通量计算形态预测包振安(Stanford)，Vijay Pande(Stanford)，Michael Toney(SSRL)非技术描述：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与轻便灵活的应用相结合。特别是，它们为欠发达国家农村地区数百万无法使用电网的人提供了基本电力的前景。文献中已经报道了许多口服脊髓灰质炎疫苗材料，但很少有材料显示效率超过8%。关键的挑战是设计出满足所有要求的材料。典型的口服脊髓灰质炎疫苗由供体和受体混合而成。预测纳米级的形态仍然是预测OPV性能的最大挑战之一。因此，目前需要筛选多种材料组合和较大的工艺参数空间(如施主/受主比、溶剂、热处理条件、薄膜厚度)。技术描述：本项目旨在为OPV材料的高通量形态预测提供一个完整的研究计划。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用于计算形态计算和实验表征的高通量技术。计算开发利用了分布式志愿者计算网络提供的海量计算能力。潘德将重新装备他的庞大的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的体相-异质结混合形态。Folding@home允许潘德和同事执行以前无法执行的计算，因为他们可以达到比传统方法可能长数千到数百万倍的时间尺度。BaO将设计合成路线来制备薄膜制备的模型化合物，并与理论预测的形貌进行比较。鲍康如和托尼将一起对这些化合物进行光电子、结构和形态测量。该表征将建立和使用新的高通量仪器。实验数据，无论是正面或负面的结果，都将提供给理论小组，在那里它将被添加到经验数据集合中。后者在校准方案中使用，并为许多采用的模型提供了参数化。扩展此数据集将改进相关的建模工作及其预测能力。这反过来将导致开发过程的调整。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了解分子结构/形态相关性提供基础，并将为口服脊髓灰质炎疫苗社区提供开放的资源。