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Abstract The intriguing functionalities of emerging quasi‐2D metal halide perovskites (MHPs) have led to further exploration of this material class for sustainable and scalable optoelectronic applications. However, the chemical complexities in precursors—primarily determined by the 2D:3D compositional ratio—result in uncontrolled phase heterogeneities in these materials, which compromises the optoelectronic performances. Yet, this phenomenon remains poorly understood due to the massive quasi‐2D compositional space. To systematically explore the fundamental principles, herein, a high‐throughput automated synthesis‐characterization workflow is designed and implemented to formamidinium (FA)‐based quasi‐2D MHP system. It is revealed that the stable 3D‐like phases, where the α‐FAPbI₃surface is passivated by 2D spacers, exclusively emerge at the compositional range (35–55% of FAPbI₃), deviating from the stoichiometric considerations. A quantitative crystallographic study via high‐throughput grazing‐incidence wide‐angle X‐ray scattering (GIWAXS) experiments integrated with automated peak analysis function quickly reveals that the 3D‐like phases are vertically aligned, facilitating vertical charge conduction that can be beneficial for optoelectronic applications. Together, this study uncovers the optimal 2D:3D compositional range for complex quasi‐2D MHP systems, realizing promising optoelectronic functionalities. The automated experimental workflow significantly accelerates materials discoveries and processing optimizations that are transferrable to other deposition methods, while providing fundamental insights into complex materials systems. 

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning. 

It is widely accepted that the interaction of swift heavy ions with many complex oxides is predominantly governed by the electronic energy loss that gives rise to nanoscale amorphous ion tracks along the penetration direction. 

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research. 

Self-driving laboratories (SDLs) are the future for scientific discovery in a world growing with artificial intelligence. The interaction between scientists and automated instrumentation are leading conversations about the impact of SDLs on research. 

Ferroelectricity in van der Waals (vdW) layered material has attracted a great deal of interest recently. CuInP2S6 (CIPS), the only vdW layered material whose ferroelectricity in the bulk was demonstrated by direct polarization measurements, was shown to remain ferroelectric down to a thickness of a few nanometers. However, its ferroelectric properties have just started to be explored in the context of potential device applications. We report here the preparation and measurements of metal-ferroelectric semiconductor-metal heterostructures using nanosheets of CIPS obtained by mechanical exfoliation. Four bias voltage and polarization dependent resistive states were observed in the current–voltage characteristics, which we attribute to the formation of ferroelectric Schottky diode, along with switching behavior. 

Low-cost self-driving labs (SDLs) offer faster prototyping, low-risk hands-on experience, and a test bed for sophisticated experimental planning software which helps us develop state-of-the-art SDLs.