论文标题
在多模式成像中学习正确的通道：压电力显微镜中的自动实验
Learning the right channel in multimodal imaging: automated experiment in Piezoresponse Force Microscopy
论文作者
论文摘要
我们报告了自动化实验工作流的开发和实验实施，以识别光谱测量现象的最佳预测通道。该方法基于结合概率预测的结合深内核学习和渠道选择的基本强化学习政策。它允许依次采样的哪些可用观察通道的识别，最可预测选定的行为，因此具有最强的相关性。我们在压电力显微镜（PFM）中实施了这种方法，以在压电响应光谱中表现出感兴趣的行为。我们说明了模型样本中的极化 - 电压磁滞回路和频率磁滞回路区域的最佳预测通道。相同的工作流程和代码是通用的，适用于任何多模式成像和局部表征方法。
We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combination of ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. It allows the identification of which of the available observational channels, sampled sequentially, are most predictive of selected behaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in Piezoresponse Force Microscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictive channel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. The same workflow and code are universal and applicable for any multimodal imaging and local characterization methods.
