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SUMMARY:Design of Experiments (DOE) and Machine Learning-assisted Organic S
 olar Cell Efficiency Optimization. How to Effectively Explore Parameter Sp
 ace
LOCATION:Chemistry A101
TZID:America/Denver
DTSTART:20194101T000000
UID:2026-05-06-14-45-08@natsci.colostate.edu
DTSTAMP:20260506T144508
Description:About the Seminar\n\nOrganic solar cells (OSCs) are a potential
  cost-effective way to transform solar energy into electricity due to thei
 r potential for low-cost and high-throughput roll-to-roll production. Impr
 oving the power conversion efficiency (PCE) and stability of OSCs are two 
 of the most important tasks on the march towards commercialization. While 
 much effort has been focused on developing new materials\, the optimizatio
 n of processing conditions is equally important\, where optimization is ty
 pically done in a haphazard manner using the experimenter\\'s \"intuition\
 " or through one-variable-at-a-time (OVAT/Edisonian) manipulation. Such me
 thods can\, however\, fail to find the maximum PCE due to the high dimensi
 onality parameter space of processing conditions and correlations between 
 parameters. Moreover\, laboratory-scale OSC fabrication is often low-throu
 ghput\, time-consuming\, artisanal in nature\, and expensive. Here we desc
 ribe the Design of Experiments (DOE) approach\, along with machine learnin
 g (ML) concepts to optimize solar cell efficiency. DoE is used to systemat
 ically explore the parameter space of processing conditions and ML is then
  utilized to estimate the PCE landscape as a function of the processing pa
 rameters. This process is then applied recursively to successively smaller
  regions of parameters space in regions of interest. Utilizing this proces
 s allows experimentalists to explore a larger parameter space with fewer e
 xperimental trials while obtaining valid and objective conclusions. Specif
 ic examples of concrete improvement of the power conversion efficiency of 
 OSCs will be described\, and generalized to any system of interest. 4:00 p
 m
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