arXiv subjects

Gerd Duscher

Publications and source records attributed to Gerd Duscher.

15 recordsLinked to original sources

arXiv2026

Hierarchical automation of scanning probe microscopy through agentic orchestration and algorithmic control

Rapid advances in agentic artificial intelligence enable scientific systems to interpret open-ended objectives, combine heterogeneous information, invoke specialized tools, and revise experimental strategies as evidence accumulates. However, physical experimentation also contains many tasks for which agentic reasoning provides little advantage and can reduce reliability. Quantitative analysis, optimization, spatial targeting, validation, and instrument execution are often better posed as deterministic or algorithmic operations with explicit objectives and verifiable outputs. Here, we introduce a hierarchical architecture for autonomous experimentation that separates these roles. Agentic components interpret scientific intent, construct task-dependent experimental representations, evaluate accumulated evidence, and select high-level actions, whereas deterministic algorithms perform numerical analysis, coordinate selection, validation, and physical execution. We implement this architecture in piezoresponse force microscopy. Starting from a broad scientific question concerning the relation between local domain structure and polarization switching, the system constructs spatial descriptors from multichannel imaging, selects and analyzes local hysteresis measurements, adapts the spectroscopy waveform, and terminates the experiment when additional measurements cease to provide new evidence. The autonomous trajectory also identifies a confounding relationship between polarization state and domain-wall proximity and recognizes that the requested contrast is not independently represented within the available field of view. These results demonstrate a route toward scientific autonomy in which agents determine what evidence is required while algorithms determine how that evidence is acquired reproducibly and within validated physical constraints.

cond-mat.mtrl-sci↗

arXiv2026

From Photons to Electrons: Accelerated Materials Discovery via Random Libraries and Automated Scanning Transmission Electron Microscopy

The real-world implementation of materials prediction algorithms remains limited by persistent characterization bottlenecks in materials discovery, where photon-based probe techniques (e.g., XRD or Raman) impose long acquisition times and access latencies, restricting exploration to quasi-ternary composition spaces typically realized as compositional libraries. Here, we argue that a paradigm shift from photon- to electron-based characterization can realign materials characterization with modern high-throughput synthesis. We formulate cost functions and exploration strategies for STEM-based chemical and structural characterization and use Monte Carlo simulations to show that random chemical libraries, where compositionally distinct regions are co-located within a single specimen and interrogated in situ by electron spectroscopies, can sample high-dimensional composition and phase spaces with orders-of-magnitude greater effective coverage than conventional spread-library/X-ray approaches. We further demonstrate autonomous discovery on a laboratory STEM platform, where ML-based autotuning and scripted control enable iterative region selection and characterization without human intervention. Finally, we outline extensions to labeled or position-encoded libraries that preserve compositional and processing metadata, enabling joint exploration of composition and process spaces. Together, these results establish electron-based, ML-enabled STEM as a scalable pathway toward combinatorially rich materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

Direct observation of ultrafast defect-bound and free exciton dynamics in defect-engineered WS$_2$ monolayers

Defects in two-dimensional transition metal dichalcogenides (TMDCs) broadly affect their optical and electronic properties. Directly capturing the ultrafast processes of exciton trapping and defect-bound exciton formation is crucial for understanding and advancing defect-mediated optoelectronics and quantum technologies. However, the weak transient optical absorption of defect-bound excitons has limited their experimental observation to date. Here, we report the direct observation of the ultrafast dynamics of defect-bound excitons in monolayer WS$_2$ crystals with a high density of mono-sulfur vacancies (V$_S$) and W-site defect complexes (S$_W$V$_S$) resulting from synthesis by alkali metal halide-assisted chemical vapor deposition. The dynamics of excitons bound to these defects, along with their coherent interactions with free excitons, are elucidated using ultrafast optical spectroscopy. Using above band-edge photoexcitation, we find that both free and defect-bound excitons simultaneously form within 300 fs from hot carrier relaxation. The defect-bound excitons exhibit shorter lifetimes than free excitons, leading to a population difference of the corresponding excitonic states and free exciton trapping within a 1--100 ps window. Band-edge photoexcitation of free and defect-bound exciton states reveals ultrafast interconversion within ~150 fs (comparable to our temporal resolution), indicating possible coherent coupling between these states. We further demonstrate efficient up-conversion of defect-bound excitons to free excitons with photon energies up to ~300 meV below the free exciton resonance. These findings provide insights into the ultrafast dynamics of defect-bound excitons in TMDCs and their coupling with free excitons, which are relevant to defect-engineered optoelectronic, quantum photonic, and valleytronic applications.

physics.optics↗

arXiv2026

Ultralight High-Entropy Nanowire Scaffolds for Extreme-Temperature Functionality

High-entropy alloys (HEAs) combine compositional disorder with exceptional functional tunability, yet their inherently high-density limits use in lightweight systems. Here, we introduce entropy-architected nanowire metamaterials, a class of materials that couple configurational entropy with structural porosity to achieve metal-like functionality at ultralow density. FeCoNiCrCu HEA nanowires were electrodeposited into porous templates and freeze-cast into three-dimensional ``bird`s-nest`` scaffolds with densities below 1 $\%$ of the bulk metal. The resulting architectures retain a disordered face-centered-cubic phase, exhibit Curie temperatures exceeding 1000 K, and deliver thermal diffusivity ($\approx0.211$ mm$^2$ s$^{-1}$) comparable to titanium alloys. Structural and spectroscopic analyses reveal nanoscale Cu segregation that enhances magnetic ordering and thermal stability. These findings demonstrate that configurational entropy and architectural hierarchy can be co-engineered to yield lightweight, high-temperature functional materials for extreme-environment applications.

cond-mat.mtrl-sci↗

arXiv2026

Towards Self-Optimizing Electron Microscope: Robust Tuning of Aberration Coefficients via Physics-Aware Multi-Objective Bayesian Optimization

Realizing high-throughput aberration-corrected Scanning Transmission Electron Microscopy (STEM) exploration of atomic structures requires rapid tuning of multipole probe correctors while compensating for the inevitable drift of the optical column. While automated alignment routines exist, conventional approaches rely on serial, gradient-free searches (e.g., Nelder-Mead) that are sample-inefficient and struggle to correct multiple interacting parameters simultaneously. Conversely, emerging deep learning methods offer speed but often lack the flexibility to adapt to varying sample conditions without extensive retraining. Here, we introduce a Multi-Objective Bayesian Optimization (MOBO) framework for rapid, data-efficient aberration correction. Importantly, this framework does not prescribe a single notion of image quality; instead, it enables user-defined, physically motivated reward formulations (e.g., symmetry-induced objectives) and uses Pareto fronts to expose the resulting trade-offs between competing experimental priorities. By using Gaussian Process regression to model the aberration landscape probabilistically, our workflow actively selects the most informative lens settings to evaluate next, rather than performing an exhaustive blind search. We demonstrate that this active learning loop is more robust than traditional optimization algorithms and effectively tunes focus, astigmatism, and higher-order aberrations. By balancing competing objectives, this approach enables "self-optimizing" microscopy by dynamically sustaining optimal performance during experiments.

cs.LG↗

arXiv2025

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

cond-mat.mtrl-sci↗

arXiv2025

Atom identification in bilayer moire materials with Gomb-Net

Moire patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, effectively deconvoluting the moire pattern. This enables layer-specific mapping of quantities like strain and dopant distributions, unlike other commonly used segmentation models which struggle with moire-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS2-WS2(1-x)Se2x heterostructure and found that layer-specific implantation sites are unaffected by the moire pattern's local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.

cond-mat.mtrl-sci↗

arXiv2025

Roadmap on Atomic-scale Semiconductor Devices

Spin states in semiconductors provide exceptionally stable and noise-resistant environments for qubits, positioning them as optimal candidates for reliable quantum computing technologies. The proposal to use nuclear and electronic spins of donor atoms in silicon, introduced by Kane in 1998, sparked a new research field focused on the precise positioning of individual impurity atoms for quantum devices, utilising scanning tunnelling microscopy and ion implantation. This roadmap article reviews the advancements in the 25 years since Kane's proposal, the current challenges, and the future directions in atomic-scale semiconductor device fabrication and measurement. It covers the quest to create a silicon-based quantum computer and expands to include diverse material systems and fabrication techniques, highlighting the potential for a broad range of semiconductor quantum technological applications. Key developments include phosphorus in silicon devices such as single-atom transistors, arrayed few-donor devices, one- and two-qubit gates, three-dimensional architectures, and the development of a toolbox for future quantum integrated circuits. The roadmap also explores new impurity species like arsenic and antimony for enhanced scalability and higher-dimensional spin systems, new chemistry for dopant precursors and lithographic resists, and the potential for germanium-based devices. Emerging methods, such as photon-based lithography and electron beam manipulation, are discussed for their disruptive potential. This roadmap charts the path toward scalable quantum computing and advanced semiconductor quantum technologies, emphasising the critical intersections of experiment, technological development, and theory.

quant-ph↗

arXiv2024

Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments

Automated experiments in scanning transmission electron microscopy (STEM) require rapid image segmentation to optimize data representation for human interpretation, decision-making, site-selective spectroscopies, and atomic manipulation. Currently, segmentation tasks are typically performed using supervised machine learning methods, which require human-labeled data and are sensitive to out-of-distribution drift effects caused by changes in resolution, sampling, or beam shape. Here, we operationalize and benchmark a recently proposed reward-driven optimization workflow for on-the fly image analysis in STEM. This unsupervised approach is much more robust, as it does not rely on human labels and is fully explainable. The explanatory feedback can help the human to verify the decision making and potentially tune the model by selecting the position along the Pareto frontier of reward functions. We establish the timing and effectiveness of this method, demonstrating its capability for real-time performance in high-throughput and dynamic automated STEM experiments. The reward driven approach allows to construct explainable robust analysis workflows and can be generalized to a broad range of image analysis tasks in electron and scanning probe microscopy and chemical imaging.

cond-mat.mtrl-sci↗

arXiv2024

Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope

Scanning Transmission Electron Microscopy (STEM) coupled with Electron Energy Loss Spectroscopy (EELS) presents a powerful platform for detailed material characterization via rich imaging and spectroscopic data. Modern electron microscopes can access multiple length scales and sampling rates far beyond human perception and reaction time. Recent advancements in machine learning (ML) offer a promising avenue to enhance these capabilities by integrating ML algorithms into the STEM-EELS framework, fostering an environment of active learning. This work enables the seamless integration of STEM with High-Performance Computing (HPC) systems. We present several implemented workflows that exemplify this integration. These workflows include sophisticated techniques such as object finding and Deep Kernel Learning (DKL). Through these developments, we demonstrate how the fusion of STEM-EELS with ML and HPC enhances the efficiency and scope of material characterization for 70% STEM available globally. The codes are available at GitHub link.

physics.ins-det↗

arXiv2024

Ultraclean suspended graphene by radiolysis of adsorbed water

Access to intrinsic properties of a 2D material is challenging due to the absence of a bulk that would dominate over surface contamination, and this lack of bulk also precludes effective conventional cleaning methods that are almost always sacrificial. Suspended graphene and carbon contaminants represent the most salient challenge. This work has achieved ultraclean graphene, attested by electron energy loss (EEL) spectra unprecedentedly exhibiting fine-structure features expected from bonding and band structure. In the cleaning process in a transmission electron microscope, radicals generated by radiolysis of intentionally adsorbed water remove organic contaminants, which would otherwise be feedstock of the notorious electron irradiation induced carbon deposition. This method can be readily adapted to other experimental settings and other materials, to enable previously inhibited undertakings that rely on the intrinsic properties or ultimate thinness of 2D materials. Importantly, the method is surprisingly simple and robust, easily implementable with common lab equipment.

cond-mat.mtrl-sci↗

arXiv2024

Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS

Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. This is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations. One of foundational problems is the discovery of nanometer- or atomic scale structures having specific signatures in EELS spectra. Here we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions on the experiment progression. In agreement with actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring automated experiment in the real and feature space of the system and monitor knowledge acquisition of the DKL model. Based on these, we construct intervention strategies, thus defining human-in the loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging.

cond-mat.mtrl-sci↗

arXiv2023

Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy

Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the APIs by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Here, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and human operator monitors experiment progression in real- and feature space of the system and tunes the policies of the ML agent to steer the experiment towards specific objectives.

cond-mat.mtrl-sci↗

arXiv2023

A processing and analytics system for microscopy data workflows: the Pycroscopy ecosystem of packages

Major advancements in fields as diverse as biology and quantum computing have relied on a multitude of microscopic techniques. All optical, electron and scanning probe microscopy advanced with new detector technologies and integration of spectroscopy, imaging, and diffraction. Despite the considerable proliferation of these instruments, significant bottlenecks remain in terms of processing, analysis, storage, and retrieval of acquired datasets. Aside from the lack of file standards, individual domain-specific analysis packages are often disjoint from the underlying datasets. Thus, keeping track of analysis and processing steps remains tedious for the end-user, hampering reproducibility. Here, we introduce the pycroscopy ecosystem of packages, an open-source python-based ecosystem underpinned by a common data model. Our data model, termed the N-dimensional spectral imaging data format, is realized in pycroscopy's sidpy package. This package is built on top of dask arrays, thus leveraging dask array attributes but expanding them to accelerate microscopy-relevant analysis and visualization. Several examples of the use of the pycroscopy ecosystem to create workflows for data ingestion and analysis are shown. Adoption of such standardized routines will be critical to usher in the next generation of autonomous instruments where processing, computation, and meta-data storage will be critical to overall experimental operations.

physics.ins-det↗

arXiv2017

High conduction hopping behavior induced in transition metal dichalcogenides by percolating defect networks: toward atomically thin circuits

Atomically thin circuits have recently been explored for applications in next-generation electronics and optoelectronics and have been demonstrated with two-dimensional lateral heterojunctions. In order to form true 2D circuitry from a single material, electronic properties must be spatially tunable. Here, we report tunable transport behavior which was introduced into single layer tungsten diselenide and tungsten disulfide by focused He$^+$ irradiation. Pseudo-metallic behavior was induced by irradiating the materials with a dose of ~1x10$^{16} He^+/cm^2$ to introduce defect states, and subsequent temperature-dependent transport measurements suggest a nearest neighbor hopping mechanism is operative. Scanning transmission electron microscopy and electron energy loss spectroscopy reveal that Se is sputtered preferentially, and extended percolating networks of edge states form within WSe$_2$ at a critical dose of 1x10$^{16} He^+/cm^2$. First-principles calculations confirm the semiconductor-to-metallic transition of WSe$_2$ after pore and edge defects were introduced by He$^+$ irradiation. The hopping conduction was utilized to direct-write resistor loaded logic circuits in WSe$_2$ and WS$_2$ with a voltage gain of greater than 5. Edge contacted thin film transistors were also fabricated with a high on/off ratio (> 10$^6$), demonstrating potential for the formation of atomically thin circuits.

cond-mat.mtrl-sci↗