CAREER: A Predictive Modeling Framework for Exploring Process-Structure-Property Relationships in Organic Solar Cells

职业生涯：用于探索有机太阳能电池工艺-结构-性能关系的预测建模框架

• 批准号: 1149365
• 负责人: Baskar Ganapathysubramanian
• 金额: $ 40万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149365&HistoricalAwards=false
• 关键词: CAREER; Predictive; Modeling; Framework; Exploring

This Faculty Early Career Development (CAREER) grant provides funding for the development of a numerical framework to model solvent-based fabrication of thin film organic solar cells. The developed numerical tools will track the evolution of the thin-film morphology (i.e. material distribution) as a function of processing conditions (evaporation rate, spinning speed, annealing time) and configuration (blend-ratio, solvent type and substrate). An adaptive time-stepping strategy along with domain-decomposition and associated parallel computing tools will be used to solve this multiscale, multiphysics problem. A computational tool, based on ideas from graph theory, will be developed to rapidly classify and characterize the predicted morphologies. The numerical predictions will be validated using available microscopy and x-ray diffraction experiments. High throughput simulations will be performed to quantify and understand the effects of solvents, substrate patterning and evaporation conditions on morphology evolution and device performance.If successful, the results of this research will lead to improvements in the performance of organic photovoltaic devices. The quantitative analysis of morphology evolution during fabrication will accelerate the design process by delivering the ability to directly predict and interpret time-varying three-dimensional snapshots of intermediate morphologies. The high throughput fabrication process-morphology analysis will provide valuable insights toward synthesis and design. The research involves formulating and solving several algorithmic and computing problems, which provide contributions to applied math and computer science in their own right. The proposed work will also educate the next generation of STEM (science, technology, engineering and mathematics) students and the predominantly experimental organic solar cell community about integrating computational thinking with experimental analysis in renewable energy research through education modules, short courses, and open source software.

该学院早期职业发展（CAREER）赠款为开发数字框架提供资金，以模拟薄膜有机太阳能电池的溶剂型制造。开发的数值工具将跟踪薄膜形态（即材料分布）随加工条件（蒸发速率、旋转速度、退火时间）和配置（混合比、溶剂类型和基材）的变化。自适应时间步进策略以及域分解和相关的并行计算工具将用于解决这个多尺度、多物理场问题。将开发一种基于图论思想的计算工具，以快速分类和表征预测的形态。数值预测将使用可用的显微镜​​和 X 射线衍射实验进行验证。将进行高通量模拟，以量化和了解溶剂、基板图案化和蒸发条件对形态演变和器件性能的影响。如果成功，这项研究的结果将导致有机光伏器件性能的改进。制造过程中形态演化的定量分析将提供直接预测和解释中间形态随时间变化的三维快照的能力，从而加速设计过程。高通量制造工艺形态分析将为合成和设计提供有价值的见解。该研究涉及制定和解决几个算法和计算问题，这些问题本身就为应用数学和计算机科学做出了贡献。拟议的工作还将通过教育模块、短期课程和开源，教育下一代 STEM（科学、技术、工程和数学）学生和以实验为主的有机太阳能电池社区，将计算思维与可再生能源研究中的实验分析相结合软件。