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：有机光伏的高通量计算形态预测Zhenan Bao（斯坦福）、Vijay Pande（斯坦福）、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，因为它们承诺低功耗成本大规模生产与轻量级和灵活的应用相结合。特别是，它们提供了向不发达国家农村地区无法接入电网的数百万人民提供基本电力的前景。文献中报道了许多 OPV 材料，但很少有效率超过 8% 的。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，目前需要筛选许多材料组合和大的工艺参数空间（例如供体/受体比率、溶剂、退火条件、膜厚度）。技术描述：该项目旨在为 OPV 高通量形貌预测提供综合研究计划材料。理论、综合和表征之间的连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巨大计算能力。 Pande 将重新装备他的大型 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，其时间尺度比传统方法长数千到数百万倍。 Bao将设计合成路线来制备用于薄膜制备的模型化合物，并与理论预测的形貌进行比较。鲍和托尼将共同对这些化合物进行光电、结构和形态测量。表征将建立并采用新的高通量仪器。实验数据，无论结果是积极还是消极，都将提供给理论小组，并将其添加到经验数据集合中。后者用于校准方案并为许多所使用的模型提供参数化。扩展该数据集将改进相关的建模工作及其预测能力。这反过来又会导致开发流程的调整。广泛的结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 社区的公开资源。