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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. 

Abstract 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. 

Abstract Controlled fabrication of nanopores in 2D materials offer the means to create robust membranes needed for ion transport and nanofiltration. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation; however, aberration‐corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a sub‐Å sized electron beam for atomic manipulation along with atomic resolution imaging. Here, a method for automated nanopore fabrication is utilized with real‐time atomic visualization to enhance the mechanistic understanding of beam‐induced transformations. Additionally, an electron beam simulation technique, Electron‐Beam Simulator (E‐BeamSim) is developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the MXene Ti₃C₂T_x, the influence of temperature on nanopore fabrication is explored by tracking atomic transformations and find that at room temperature the electron beam irradiation induces random displacement and results in titanium pileups at the nanopore edge, which is confirmed by E‐BeamSim. At elevated temperatures, after removal of the surface functional groups and with the increased mobility of atoms results in atomic transformations that lead to the selective removal of atoms layer by layer. This work can lead to the development of defect engineering techniques within functionalized MXene layers and other 2D materials. 

Abstract Heteroanionic oxysulfide perovskite compounds represent an emerging class of new materials allowing for a wide range of tunability in the electronic structure that could lead to a diverse spectrum of novel and improved functionalities. Unlike cation ordered double perovskites—where the origins and design rules of various experimentally observed cation orderings are well known and understood—anion ordering in heteroanionic perovskites remains a largely uncharted territory. In this contribution, we present and discuss insights that have emerged from our first-principles-based electronic structure analysis of a prototypical anion-ordered SrHf(O_0.5S_0.5)₃oxysulfide chemistry, studied in all possible anion configurations allowed within a finite size supercell. We demonstrate that the preferred anion ordering is always an all-cisarrangement of anions around an HfO₃S₃octahedron. As a general finding beyond the specific chemistry, the origins of this ordering tendency are traced back to a combined stabilization effect stemming from electronic, elastic, and electrostatic contributions. These qualitative notions are also quantified using state-of-the-art machine learning models. We further study the relative stability of the identified ordering as a function of A (Ca, Sr, Ba) and B (Ti, Zr, Hf) site chemistries and probe chemistry-dependent trends in the electronic structure and functionality of the material. Most remarkably, we find that the identified ground-state anion ordering breaks the inversion symmetry to create a family of oxysulfide ferroelectrics with a macroscopic polarization >30 μC/cm², exhibiting a significant promise for electronic materials applications.