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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.

 

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.

 

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

 

The unique physical properties of two-dimensional (2D) metal halide perovskites (MHPs) such as nonlinear optics, anisotropic charge transport, and ferroelectricity have made these materials promising candidates for multifunctional applications. Recently, fluorine derivatives such as 4,4-difluoropiperidinium lead iodide perovskite or (4,4-DFPD, C 5 H 10 F 2 N) 2 PbI 4 have shown strong ferroelectricity as compared to other 2D MHPs. Although it was previously addressed that the ferroelectricity in MHPs can be affected by illumination, the underlying physical mechanisms of light–ferroelectricity interaction in 2D MHPs are still lacking. Here, we explore the electromechanical responses in 4,4-(DFPD) 2 PbI 4 thin films using advanced scanning probe microscopy techniques revealing ferroelectric domain structures. Hysteretic ferroelectric loops measured by contact-Kelvin probe force microscopy are dependent on domain structures under dark conditions, while ferroelectricity weakens under illumination. The X-ray diffraction patterns exhibit significant changes in preferential orientation of individual lattice planes under illumination. Particularly, the reduced intensity of the (1 1 1) lattice plane under illumination leads to transitioning from a ferroelectric to a paraelectric phase. The instability of positive ions, especially molecular organic cations, is observed under illumination by time-of-flight secondary ion mass spectrometry. The combination of crystallographic orientation and chemical changes under illumination clearly contributes to the origin of light–ferroelectricity interaction in 2D (4,4-DFPD, C 5 H 10 F 2 N) 2 PbI 4 .