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

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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.