A data-driven identification of morphological features influencing the fill factor and efficiency of organic photovoltaic devices

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• 作者：Ryan S. Gebhardt^a ; ^{ryangeb16@gmail.com} ; Pengfei Du^b ; ^{pengfeidu83@gmail.com} ; Olga Wodo^c ; ^{olga.wodo@gmail.com} ; Baskar Ganapathysubramanian^b ; ^{baskarg@iastate.edu}
• 关键词：Organic photovoltaics ; Morphology ; Graph methods ; Drift diffusion models ; Fill factor ; Correlation analysis
• 刊名：Computational Materials Science
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：129
• 期：Complete
• 页码：220-225
• 全文大小：1443 K
• 卷排序：129

文摘

Morphological traits that correlate with short circuit current and fill factor are identified. This is accomplished by creating a large data set of synthetic bulk heterojunction morphologies. Each morphology is characterized into descriptors via graph-based methods. The photovoltaic performance of each morphology is simulated with a drift-diffusion model. Correlation analysis is used to identify descriptors that correlate with device performance.