Comparing Hyper-optimized Machine Learning Models for Predicting
  Efficiency Degradation in Organic Solar Cells
David Valientea Fernando Rodríguez-Mas Juan V. Alegre-Requena David Dalmau Juan C. Ferrer
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 of automated ML
protocols that execute sequentially against our database by simply command-line
interface. This easily permits hyper-optimizing and randomizing seeds of the ML
models through exhaustive benchmarking so that optimal models are obtained. The
accuracy achieved reaches values of the coefficient determination (R2) widely
exceeding 0.90, whereas the root mean squared error (RMSE), sum of squared
error (SSE), and mean absolute error (MAE)>1% of the target value, the PCE.
Additionally, we contribute with validated models able to screen the behavior
of OSCs never seen in the database. In that case, R2~0.96-0.97 and RMSE~1%,
thus confirming the reliability of the proposal to predict. For comparative
purposes, classical Bayesian regression fitting based on non-linear mean
squares (LMS) are also presented, which only perform sufficiently for
univariate cases of single OSCs. Hence they fail to outperform the breadth of
the capabilities shown by the ML models. Finally, thanks to the standardized
results offered by the ML framework, we study the dependencies between the
variables of the dataset and their implications for the optimal performance and
stability of the OSCs. Reproducibility is ensured by a standardized report
altogether with the dataset, which are publicly available at Github.