论文标题
通过主动学习驱动的自动扫描探针显微镜探索金属卤化物钙钛矿中电导率和滞后的显微结构起源
Exploring the microstructural origins of conductivity and hysteresis in metal halide perovskites via active learning driven automated scanning probe microscopy
论文作者
论文摘要
金属卤化物钙钛矿（MHP）中的电子传输和磁滞是光伏，发射器件以及光和化学传感器中应用的关键。这些现象受到材料微结构的强烈影响，包括晶界，铁域域壁和二级夹杂物。在这里，我们演示了一个主动的机器学习框架，用于“驾驶”自动扫描探针显微镜（SPM），以发现负责MHP中运输行为特定方面的微观结构。在我们的设置中，显微镜可以发现最大化传导，磁滞或任何其他可以从一组电流 - 电压光谱中得出的特征的显微结构元素。这种方法为SPM探索复杂材料中材料功能的起源开辟了新的机会，并且可以与其他特征技术集成（先验知识）或之后（识别详细研究的识别）功能探测。
Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials microstructure including grain boundaries, ferroic domain walls, and secondary phase inclusions. Here, we demonstrate an active machine learning framework for 'driving' an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in MHPs. In our setup, the microscope can discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic that can be derived from a set of current-voltage spectra. This approach opens new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques either before (prior knowledge) or after (identification of locations of interest for detail studies) functional probing.
