Summary of Comparing Hyper-optimized Machine Learning Models For Predicting Efficiency Degradation in Organic Solar Cells, by David Valiente et al.
Comparing Hyper-optimized Machine Learning Models for Predicting Efficiency Degradation in Organic Solar Cells
by David Valiente, Fernando Rodríguez-Mas, Juan V. Alegre-Requena, David Dalmau, Juan C. Ferrer
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a set of machine learning models that can accurately predict the temporal degradation of power conversion efficiency (PCE) in polymeric organic solar cells (OSCs). To achieve this, a database was generated containing over 996 entries with up to 7 variables related to manufacturing processes and environmental conditions for more than 180 days. A software framework was then used to automate machine learning protocols, allowing for hyper-optimization and randomizing seeds through exhaustive benchmarking. The resulting models achieved high accuracy, with R2 values exceeding 0.90 and low errors (RMSE, SSE, MAE) compared to the target PCE value. Additionally, validated models were developed that can screen the behavior of unseen OSCs, confirming the reliability of the proposal. Classical Bayesian regression fitting based on non-linear mean squares was also attempted but failed to outperform the machine learning models. The paper concludes by studying the dependencies between variables in the dataset and their implications for optimal performance and stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how machine learning can be used to predict the degradation of solar cells over time. It does this by creating a big database with lots of information about different solar cells and then using that data to train special computer programs to make predictions. The results are really good, with the models being able to accurately predict what will happen to a solar cell even if it’s never been seen before. This is important because it could help us make better solar panels in the future. |
Keywords
» Artificial intelligence » Machine learning » Mae » Optimization » Regression
