Comparing Hyper-optimized Machine Learning Models for Predicting Efficiency Degradation in Organic Solar Cells

arXiv:2404.00173v1 Announce Type: new
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