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Unraveling Correlations between Molecular Properties and Device Parameters of Organic Solar Cells Using Machine Learning
journal contribution
posted on 2019-11-11, 21:45 authored by Harikrishna Sahu, Haibo MaUnderstanding
the relationships between molecular properties and
device parameters is highly desired not only to improve the overall
performance of an organic solar cell but also to fulfill the requirements
of a device for a particular application such as solar-to-fuel energy
conversion (high open-circuit voltage (VOC)) or solar window applications (high short circuit current (JSC)). In this work, a series of machine learning
models are built for three important device characteristics (VOC, JSC, and fill
factor) using 13 crucial molecular properties as descriptors, resulting
in an impressive predictive performance (r = 0.7).
These models may play a vital role in designing promising organic
materials for a specific photovoltaic application with high VOC/JSC. The importance
of descriptors for each device parameter is unraveled, which may assist
in tuning them and improve understanding of the energy conversion
process.
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device parameterdevice parameterssolar-to-fuel energy conversionDevice Parametersopen-circuit voltageperformancewindow applicationsJ SCMolecular PropertiesUnraveling Correlationsdevice characteristicsOrganic Solar Cellsphotovoltaic applicationMachine Learningdescriptorenergy conversion processmodelV OC
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