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.

翻译：本研究提出了一套优化的机器学习模型，用于表征具有ITO/PEDOT:PSS/P3HT:PCBM/Al多层结构的聚合物有机太阳能电池功率转换效率随时间衰减的规律。为此，我们构建了包含996条记录的数据集，涵盖长达180天内的制造工艺与环境条件等7个变量。通过集成自动化机器学习协议的软件框架，我们仅需命令行界面即可对数据集执行序列化处理。该方法通过穷举式基准测试实现了机器学习模型的超参数优化与随机种子配置，从而获得最优模型。所得模型的判定系数普遍超过0.90，而均方根误差、平方和误差与平均绝对误差均低于目标值PCE的1%。此外，我们建立了经过验证的预测模型，能够有效评估数据集中未出现过的OSC行为表现，该场景下判定系数达0.96-0.97，均方根误差约1%，证实了预测方案的可靠性。作为对比，本研究同时展示了基于非线性最小二乘法的经典贝叶斯回归拟合方法，该方法仅在单OSC单元变量分析中表现尚可，但无法超越机器学习模型所展现的综合预测能力。最后，依托机器学习框架提供的标准化结果，我们深入分析了数据集变量间的依赖关系及其对OSC最优性能与稳定性的影响。本研究通过Github公开了数据集及标准化报告，确保了研究的可复现性。