论文标题
监督学习应用于高维毫米波瞬态吸收数据，以预测钙钛矿薄膜的年龄
Supervised learning applied to high-dimensional millimeter wave transient absorption data for age prediction of perovskite thin-film
论文作者
论文摘要
我们已经分析了有限的120 GHz样品集和150 GHz时间分辨的毫米波（MMW）光导衰变（MMPCD）信号，该信号使用脉冲532-nm Laser-Laser-Laser-Laser-laseRence 10.6 Microce-June plyles 10 nm厚300 nm厚的空气稳定的封装的钙钛矿膜（甲基 - 乳腺铅）膜（甲基甲基 - 铅）。我们将直接从获得的MMPCD动力学跟踪数据及其步骤响应中得出的12个参数与基于实验日期的样本时代相关。在高斯过程回归（GPR）机器学习模型中，最终选择了与样本年龄高的五个参数，以预测样品年龄。衰老的影响（在膜产生后0到40,000小时之间）主要是根据峰值电压的变化，响应比（电导参数），损耗补偿的传输系数和瞬态本身的射频（RF）区域（FRUX）进行量化的。还显示了其他步骤反应参数的变化和衰老瞬变的衰减长度。发现GPR模型可以使用此方法对样品年龄的前进预测很好地工作。值得注意的是，用于监督学习的2 GPR内核的Matern-5为R平方左右提供了最佳的年龄预测解决方案。
We have analyzed a limited sample set of 120 GHz, and 150 GHz time-resolved millimeter wave (mmW) photoconductive decay (mmPCD) signals of 300 nm thick air-stable encapsulated perovskite film (methyl-ammonium lead halide) excited using a pulsed 532-nm laser with fluence 10.6 micro-Joules per cm-2. We correlated 12 parameters derived directly from acquired mmPCD kinetic-trace data and its step-response, each with the sample-age based on the date of the experiment. Five parameters with a high negative correlation with sample age were finally selected as predictors in the Gaussian Process Regression (GPR) machine learning model for prediction of the age of the sample. The effects of aging (between 0 and 40,000 hours after film production) are quantified mainly in terms of a shift in peak voltage, the response ratio (conductance parameter), loss-compensated transmission coefficient, and the radiofrequency (RF) area of the transient itself (flux). Changes in the other step-response parameters and the decay length of the aging transients are also shown. The GPR model is found to work well for a forward prediction of the age of the sample using this method. It is noted that the Matern-5 over 2 GPR kernel for supervised learning provides the best realistic solution for age prediction with R squared around 0.97.
