Roccapriore Kevin M, Dyck Ondrej, Oxley Mark P, Ziatdinov Maxim, Kalinin Sergei V
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
ACS Nano. 2022 May 24;16(5):7605-7614. doi: 10.1021/acsnano.1c11118. Epub 2022 Apr 27.
Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and "intelligent" probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. We verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS. This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.
在4D扫描透射电子显微镜（STEM）中进行自动化实验，以快速发现复杂材料中的局部结构、对称性破缺畸变以及内部电场和磁场。深度核学习能够主动学习局部结构与基于4D-STEM的描述符之间的关系。借此，能够对不同的结构元素进行高效且“智能”的探测，以发现所需的物理功能。这种方法允许在预定的物理现象（如最强电场强度）的引导下，以自动化方式有效地对样品进行导航，或者以探索性方式进行导航。我们首先在预先获取的4D-STEM数据上验证该方法，并进一步在运行中的STEM上进行实验实施。使用石墨烯展示了实验发现工作流程，随后将其扩展到一种鲜为人知的层状二维范德华材料MnPS。这种方法为物理驱动的自动化4D-STEM实验建立了一条途径，该实验能够探测强关联系统以及量子材料和器件的物理特性，还能用于对束敏感材料的探索。
