论文标题
通过机器学习，金属卤化物钙钛矿中光发射动力学的定量预测
Quantitative predictions of photo-emission dynamics in metal halide perovskites via machine learning
论文作者
论文摘要
金属卤化物钙钛矿（MHP）光电子可能会成为标准基于SI的技术的可行替代方案，但是目前缺乏长期稳定性排除了其商业采用。暴露于标准的操作应力源（光，温度，偏置，氧气和水）通常会激发光学和电子动力学，呼吁对MHP的光物理过程进行系统研究，并开发其预测的定量模型。我们解决了甲基铵铅三纤维胺和三碘化物薄膜的水分驱动的光发射动力学，这是相对湿度（RH）的函数。借助湿度和光致发光时间序列，我们训练复发性神经网络，并确定了它们在12小时内定量预测未来光发射路径的能力。共同，我们的原位RH-PL测量和机器学习预测模型为未来稳定的Perovskite设备的合理设计提供了一个框架，从而更快地过渡到商业应用。
Metal halide perovskite (MHP) optoelectronics may become a viable alternative to standard Si-based technologies, but the current lack of long-term stability precludes their commercial adoption. Exposure to standard operational stressors (light, temperature, bias, oxygen, and water) often instigate optical and electronic dynamics, calling for a systematic investigation into MHP photophysical processes and the development of quantitative models for their prediction. We resolve the moisture-driven light emission dynamics for both methylammonium lead tribromide and triiodide thin films as a function of relative humidity (rH). With the humidity and photoluminescence time series, we train recurrent neural networks and establish their ability to quantitatively predict the path of future light emission with <11% error over 12 hours. Together, our in situ rH-PL measurements and machine learning forecasting models provide a framework for the rational design of future stable perovskite devices and, thus, a faster transition towards commercial applications.
