Kevin M. Roccapriore

Alloying is an age-old strategy for synthesizing materials with enhanced properties. Recently, multicomponent systems such as high-entropy oxides have garnered widespread attention due to their tunable and often superior properties compared to their constituent oxides. Here, we study the local structural and chemical nuances of six-component (Mg_0.167Co_0.167Ni_0.167Cu_0.167Zn_0.167Mn_0.167)O and (Mg_0.167Co_0.167Ni_0.167Cu_0.167Zn_0.167Cr_0.167)O thin films. The Mn-alloyed thin film exhibits a higher exchange bias and greater magnetic frustration compared with the Cr-containing thin film. Scanning/transmission electron microscopy investigations reveal that the Mn-alloyed thin film exhibits the coexistence of rock salt and spinel-like regions, unlike the single-phase rock salt structure observed in the Cr-alloyed thin film. Electron energy loss spectroscopy indicates changes in Co and Mn valences within the Mn-containing thin film, suggesting the presence of mixed-valence states, which are further confirmed by X-ray absorption spectroscopy measurements. These observations are further validated by cation-site-preference energy calculations using density functional theory. Our results demonstrate how the chemistry, site occupations, and cation valences result in pronounced changes in the overall properties of high-entropy oxides.

Controlling individual atoms using lasers¹, ion traps² and scanning probe tips³ has transformed our understanding of matter and enabled breakthroughs in quantum science^4-6. Extending this control into three-dimensional (3D) solids and across mesoscopic scales, however, remains a foundational challenge. Electron irradiation in electron microscopes is known to induce atomic displacements⁷, and atomic manipulation has been proposed⁸ and demonstrated^9,10. Yet repeated and deterministic control has remained elusive^9-17. Here we demonstrate deterministic atomic engineering in a 3D crystal, creating ordered arrangements of more than 40,000 user-defined defects within minutes across a 150 nm × 100 nm × 13 nm volume. By steering individual Cr atoms in the magnetic semiconductor CrSBr into selected interstitial sites using an electron beam directed with sub-20-pm-scale accuracy, we create vacancy-interstitial complexes. The resulting impurity array forms a mesoscale crystal embedded within the host lattice, a new form of engineered artificial matter that remains stable at room temperature and outside the microscope. By tracking Cr atom displacements, we identify conditions under which the defect structures are predictable. Our calculations suggest that these defects form correlated impurity states with intra-defect optical transitions and inter-defect kinetic and Coulomb interactions. This establishes a generalizable platform for atomic defect engineering at mesoscopic, and potentially macroscopic, scales, opening opportunities for scalable quantum technologies, including deterministic colour-centre placement, quantum simulation of many-body lattice models and atomic-scale manufacturing.

The ability to probe and control matter at the picometer scale is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy offers powerful capabilities for materials analysis and modification, but sample damage, drift, and scan distortions hinder single atom analysis and deterministic manipulation. Materials analysis and modification via electron-solid interactions could be transformed by precise electron delivery to a specified atomic location, maintaining the beam position despite drift, and minimizing collateral dose. Here we develop a fast, low-dose, sub-20-pm precision electron beam positioning technique, atomic lock-on, (ALO), which offers the ability to position the beam on a specific atomic column without previously irradiating that column. We use this technique to lock onto the same selected atomic location to repeatedly measure its weak electron energy loss signal despite sample drift. Moreover, we quantitatively measure electron beam matter interactions of single atomic events with microsecond time resolution. This enables us to observe single atom dynamics such as atomic bistability in the electron microscope, revealing partially bonded atomic configurations and recapture phenomena. We discuss the prospects for high-precision measurements and deterministic control of matter for quantum technologies using electron microscopy.

The ability to probe and control matter at the picometer scale is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy offers powerful capabilities for materials analysis and modification, but sample damage, drift, and scan distortions hinder single atom analysis and deterministic manipulation. Materials analysis and modification via electron–solid interactions can be transformed by precise delivery of electrons to a specified atomic location, maintaining the beam position despite drift, and minimizing collateral dose. Here a fast, low‐dose, sub‐20‐pm precision electron beam positioning technique is developed, “atomic lock‐on,” (ALO), which offers the ability to position the beam on a specific atomic column without previously irradiating that column. This technique is used to lock onto a single selected atomic location to repeatedly measure its weak electron energy loss signal despite sample drift. Moreover, electron beam‐matter interactions in single atomic events are measured with time resolution. This enables observation of single‐atom dynamics, such as atomic bistability, revealing partially bonded atomic configurations and recapture phenomena. This opens prospects for using electron microscopy for high‐precision measurements and deterministic control of matter for quantum technologies.

A machine learning approach is introduced to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM). This method is data‐centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which is used to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre‐determined target destinations. Empirical analyses are presented that demonstrate the efficacy and generality of the approach.

Electron energy loss spectroscopy (EELS) in the scanning transmission electron microscope (STEM) is an invaluable tool for measuring material properties at nanometer and better spatial resolution and milli-electron volt energy resolution. With recent monochromation technology, energy resolution has improved even further to gain access to optical and vibrational signals down to the tens of meV while still retaining atomic resolution [ 1]. To date, very few specimens have been investigated at the single defect level – only those that remain highly stable under the electron beam have been amenable to the usual strategy of grid sampling (aka, spectrum imaging or EELS mapping) [ 2]. A significant barrier remains to probe even moderately beam-sensitive specimens as well as transient and metastable states induced by the electron beam. Such atomic and defect configurations can cause substantial electronic structures changes at the local level and are therefore of significant interest.

This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS ₂ , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.

Two-dimensional (2D) halide perovskites (HPs) are now an emerging materials system that exhibits intriguing optoelectronic functionalities. Conventionally, they have been synthesized with linear and/or planar molecular spacers, rendering nominal modifications in optoelectronic properties. In contrast, lower dimensional HPs (0D and 1D) have been accommodating to the incorporation of bulky molecular spacers, leaving fundamental insights to remain elusive in their application for unconventional 2D HP structures. Herein, by implementing a high-throughput autonomous exploration workflow, crystallization behaviors of 2D HPs based on bulky 3,3-diphenylpropylammonium (DPA) spacer are comprehensively explored. Counterintuitive to the conventional HP chemistry, synthesis of 2D DPA2PbI4 HP is indeed feasible when the steric hindrance is mediated by minute incorporation of 3D HPs. Furthermore, a Moiré superlattice is observed from the DPA2PbI4 flakes, indicating the spontaneous formation of twisted stacks of 2D HPs – the first time to the best our knowledge. We hypothesize that the unconventional van der Waals surface of DPA2PbI4 facilitates the self-assembly of the twisted stacks of 2D HPs. This work exemplifies how high-throughput experimentation can discover unconventional material systems where the synthetic principle lies beyond the conventional chemical intuition. Furthermore, these findings provide hints on how to ‘chemically’ manipulate the twist stacking in 2D HPs, thus rendering a straightforward way for bespoke realization of functionalities in exotic materials systems via bottom-up approach.