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Comparing Hyper-optimized Machine Learning Models for Predicting Efficiency Degradation in Organic Solar Cells
April 2, 2024, 7:41 p.m. | David Valientea, Fernando Rodr\'iguez-Mas, Juan V. Alegre-Requena, David Dalmau, Juan C. Ferrer
cs.LG updates on arXiv.org arxiv.org
Abstract: This work presents a set of optimal machine learning (ML) models to represent the temporal degradation suffered by the power conversion efficiency (PCE) of polymeric organic solar cells (OSCs) with a multilayer structure ITO/PEDOT:PSS/P3HT:PCBM/Al. To that aim, we generated a database with 996 entries, which includes up to 7 variables regarding both the manufacturing process and environmental conditions for more than 180 days. Then, we relied on a software framework that brings together a conglomeration …
abstract aim arxiv cells conversion cs.lg database efficiency generated ito machine machine learning machine learning models power set solar temporal type work
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