用机器学习优化无空穴层钙钛矿太阳能电池，兼顾效率与稳定性。
Simultaneous Optimization of Efficiency and Degradation in Tunable HTL-Free Perovskite Solar Cells with MWCNT-Integrated Back Contact Using a Machine Learning-Derived Polynomial Regressor
- 基于多项式回归的机器学习框架，联合实验与模拟优化参数。
- 效率提升至16.84%，1000小时衰减率降至2.39%。
- 可识别最优配置，适合光伏材料研发人员参考。
无空穴传输层（HTL）的钙钛矿太阳能电池（PSCs）因其低成本和高稳定性成为传统结构的替代方案，仅需吸收层与电子传输层（ETL）。本研究提出一种机器学习驱动的优化框架，结合实验验证与数值模拟，实现效率与稳定性的协同优化。通过调节甲胺（MA）摩尔分数、吸收层缺陷密度、厚度及ETL掺杂浓度，构建包含1650个样本的数据集，目标为效率与50小时衰减率。四次多项式回归器（PR-4）表现最佳，效率与衰减的均方根误差分别为0.0179与0.0117，决定系数分别为1与0.999。该模型具备外推能力，用于加权目标函数的L-BFGS-B优化算法，使器件效率从13.7%提升至16.84%，1000小时衰减率由6.61%降至2.39%。最后，数据集被划分为优/劣类别，多层感知机（MLP）分类器达到100%准确率，成功识别最优结构。
原文摘要 · Abstract (English)
Perovskite solar cells (PSCs) without a hole transport layer (HTL) offer a cost-effective and stable alternative to conventional architectures, utilizing only an absorber layer and an electron transport layer (ETL). This study presents a machine learning (ML)-driven framework to optimize the efficiency and stability of HTL-free PSCs by integrating experimental validation with numerical simulations. Excellent agreement is achieved between a fabricated device and its simulated counterpart at a molar fraction \( x = 68.7\% \) in \(\mathrm{MAPb}_{1-x}\mathrm{Sb}_{2x/3}\mathrm{I}_3\), where MA is methylammonium. A dataset of 1650 samples is generated by varying molar fraction, absorber defect density, thickness, and ETL doping, with corresponding efficiency and 50-hour degradation as targets. A fourth-degree polynomial regressor (PR-4) shows the best performance, achieving RMSEs of 0.0179 and 0.0117, and \( R^2 \) scores of 1 and 0.999 for efficiency and degradation, respectively. The derived model generalizes beyond the training range and is used in an L-BFGS-B optimization algorithm with a weighted objective function to maximize efficiency and minimize degradation. This improves device efficiency from 13.7\% to 16.84\% and reduces degradation from 6.61\% to 2.39\% over 1000 hours. Finally, the dataset is labeled into superior and inferior classes, and a multilayer perceptron (MLP) classifier achieves 100\% accuracy, successfully identifying optimal configurations.
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