Electronic and ionic transport underpins functionality of broad range of electronic and energy devices, and is an active field of applied and fundamental research. The lecture series on Kelvin probe force microscopy and scanning probe microscopy (SPM) based current-voltage (I-V) transport measurements introduces the basic principles of SPM techniques for transport measurements based on potential and current detection, describes the multitude of dynamics variants of KPFM, KPFM-based transport measurements, and its implementation in ambient, vacuum, and liquid environments and associated artifacts. Multidimensional current- and capacitance-based transport measurements are described. The lectures are available at YouTube: https://www.youtube.com/playlist?list=PLS6ZvEWHZ3OOkRFPTrnsV3Ej09UGUjor3.

Scanning probe microscopies (SPM) have opened the window into characterization of nanoscale functionalities of a broad panoply of materials and devices. This lecture series summarizes the principles, theoretical basis, techniques, and applications of piezoresponse force microscopy and electrochemical strain microscopies, the SPM techniques based on the electromechanical detection. The image formation mechanisms, spectroscopic and excitation modes, imaging in ambient, liquid, and vacuum environments and associated artifacts are discussed, as are application to broad ranges of materials systems from ferroelectrics to energy and information technology materials to biological systems. The lectures are available at YouTube: https://www.youtube.com/playlist?list=PLS6ZvEWHZ3OPifI9LVKnHX7tK38iTltOD.

The use of statistical/machine learning (ML) approaches to materials science is experiencing explosive growth. Here, we review recent work focusing on the generation and application of libraries from both experiment and theoretical tools. The library data enables classical correlative ML and also opens the pathway for exploration of underlying causative physical behaviors. We highlight key advances facilitated by this approach and illustrate how modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems. These developments point toward a data-driven future wherein knowledge can be aggregated and synthesized, accelerating the advancement of materials science.

Atomically resolved imaging of materials enabled by the advent of aberration-corrected scanning transmission electron microscopy (STEM) has become a mainstay of modern materials science. However, much of the wealth of quantitative information contained in the fine details of atomic structure or spectra remains largely unexplored. In this article, we discuss new opportunities enabled by physics-informed big data and machine learning technologies to extract physical information from static and dynamic STEM images, ranging from statistical thermodynamics of alloys to kinetics of solid-state reactions at a single defect level. The synergy of deep-learning image analytics and real-time feedback further allows harnessing beam-induced atomic and bond dynamics to enable direct atom-by-atom fabrication. Examples of direct atomic motion over mesoscopic distances, engineered doping at selected lattice sites, and assembly of multiatomic structures are reviewed. These advances position the scanning transmission electron microscope to transition from a mere imaging tool toward a complete nanoscale laboratory for exploring electronic, phonon, and quantum phenomena in atomically engineered structures.

Ferroelectric single-crystal-architecture-in-glass is a new class of metamaterials that would enable active integrated optics if the ferroelectric behavior is preserved within the confines of glass. We demonstrate using lithium niobate crystals fabricated in lithium niobosilicate glass by femtosecond laser irradiation that not only such behavior is preserved, the ferroelectric domains can be engineered with a DC bias. A piezoresponse force microscope is used to characterize the piezoelectric and ferroelectric behavior. The piezoresponse correlates with the orientation of the crystal lattice as expected for unconfined crystal, and a complex micro- and nano-scale ferroelectric domain structure of the as-grown crystals is revealed.

An emerging family of materials—metal halide perovskites (MHPs)—have made incredible achievements in optoelectronics in the past decade. Owing to its potential role in optoelectronic properties, the ferroic state of MHPs has been investigated by lots of researchers. Here, we review the literature regarding investigations into possible ferroic behaviors in MHPs. We summarize the recent discoveries of ferroic twin domains in MHPs. We examine the ferroelasticity and the ferroelectricity of these twin domains. Several properties relevant to the twin domains are critically analyzed, including crystallographic structure, mechanical variation, chemical variation, etc. Finally, we discussed the effects of these domains on materials’ optoelectronic properties and their potential roles in photovoltaic action.

Scanning transmission electron microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized and extended defects to mapping order parameter fields. In recent years, attention has focused on the potential of STEM to explore beam induced chemical processes and especially manipulating atomic motion, enabling atom-by-atom fabrication. These applications, as well as traditional imaging of beam sensitive materials, necessitate increasing the dynamic range of STEM in imaging and manipulation modes, and increasing the absolute scanning speed which can be achieved by combining sparse sensing methods with nonrectangular scanning trajectories. Here we have developed a general method for real-time reconstruction of sparsely sampled images from high-speed, noninvasive and diverse scanning pathways, including spiral scan and Lissajous scan. This approach is demonstrated on both the synthetic data and experimental STEM data on the beam sensitive material graphene. This work opens the door for comprehensive investigation and optimal design of dose efficient scanning strategies and real-time adaptive inference and control of e-beam induced atomic fabrication.