Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian Process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian Processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. The discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods, and can be particularly impactful for destructive or irreversible measurements.
翻译:机器学习方法的最近进展,以及扫描探测器显微镜(SPM)的可编程界面的出现,使自动和自主微镜的显微镜的自动和自主化,成为科学界关注的焦点;然而,使自动显微镜需要开发针对具体任务的机器学习方法,了解物理发现和机器学习之间的相互作用,以及完全界定的发现工作流程。这反过来又需要平衡域科学家的物理直觉和先前知识与确定实验目标和机器学习算法的奖赏,这些奖赏可以将这些目标和算法转化为具体的实验协议。在这里,我们讨论巴耶西亚积极学习的基本原则,并展示其适用于SPM的应用。我们从高萨进程发展为简单的数据驱动方法和物理模型的巴耶斯猜想,以此作为基于物理的功能适合更复杂的深核内核学习方法、结构高空过程和假设学习方法的延伸。这些框架允许使用先前数据,发现在光谱数据中编码的具体功能,并探索实验期间显示的物理法律。讨论的框架可以普遍应用,特别是用于将影视和影视系统综合的所有技术、摄像学和摄像学方法。