机器学习发现铯含量越低，卤化物钙钛矿热稳定性越高
Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskites
- 用机器学习分析高通量原位荧光数据，挖掘成分与稳定性关系
- XGBoost模型预测准确率达85%以上，证实铯含量与热稳定性负相关
- 适用于各类钙钛矿，可大幅缩短光伏材料筛选时间
卤化物钙钛矿在环境应力下表现出不可预测的性质，源于多种成分依赖的退化机制。本文结合数据可视化与机器学习技术，基于高通量原位环境光致发光（PL）实验，揭示了成分、温度与材料性能间的意外关联。相关性热图显示铯（Cs）含量对薄膜降解有显著影响，降维可视化方法识别出清晰的成分基数据聚类。极端梯度提升算法（XGBoost）在十种钙钛矿薄膜上实现成分无关（>85%准确率）和成分依赖（>75%准确率）的PL特征预测，且成分贡献度最高达99%。该模型验证了此前未被发现的铯含量与材料热稳定性间的反向相关性。本机器学习框架可推广至任意钙钛矿家族，显著减少光伏稳定材料筛选所需分析时间。
原文摘要 · Abstract (English)
Halide perovskites exhibit unpredictable properties in response to environmental stressors, due to several composition-dependent degradation mechanisms. In this work, we apply data visualization and machine learning (ML) techniques to reveal unexpected correlations between composition, temperature, and material properties while using high throughput, in situ environmental photoluminescence (PL) experiments. Correlation heatmaps show the strong influence of Cs content on film degradation, and dimensionality reduction visualization methods uncover clear composition-based data clusters. An extreme gradient boosting algorithm (XGBoost) effectively forecasts PL features for ten perovskite films with both composition-agnostic (>85% accuracy) and composition-dependent (>75% accuracy) model approaches, while elucidating the relative feature importance of composition (up to 99%). This model validates a previously unseen anti-correlation between Cs content and material thermal stability. Our ML-based framework can be expanded to any perovskite family, significantly reducing the analysis time currently employed to identify stable options for photovoltaics.
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