Seminar presentations
14.10.2025
Berit H. Goodge (Max Planck Institute for Chemical Physics of Solids, Germany)
Pushing the limits of high-resolution electron microscopy for lattice and electronic insights to quantum materials
The rich properties of strongly correlated – or often so-called quantum – materials derive from complex interplay between atomic lattice, charge, spin, and orbital interactions. The scanning transmission electron microscope provides access to all of these order parameters down to the atomic scale across a range of sample geometries. Extending local and precise structural and electronic measurements to condensed matter systems therefore promises a powerful method to disentangle the effects of competing interactions, particularly at or near phases and phase boundaries which are characterized by nanoscale inhomogeneity. This capability has proven crucial for recent progress in newly discovered families of nickelate superconductors [1-6]. Many such phases, however – such as superconductivity, charge order, magnetism, or electronic transitions – emerge only at temperatures lower than the typical ambient operating conditions or in materials which are sensitive to damage or modification by the primary electron probe. These constraints demand the development and application of new experimental and data analytical approaches for “signal-limited” STEM and EELS measurements. For low temperature and other in situ experiments, the need to outrun reduced mechanical stability imposes strict limitations on the total collection time, thereby reducing the practically achievable STEM or EELS signal. Experimentally, improved in situ sample holders can expand the available measurement window, while advances in fast, low-noise detectors dramatically improve the quality of data which can be acquired therein [7,8]. Together, these advances open the door to new insights to the local lattice and electronic structure of novel quantum materials inaccessible by any other probe, examples of which will be discussed here [9,10].
1. Goodge et al., PNAS 118(2), e2007683118 (2021).
2. Goodge et al., Nat. Mater. 22, 466–473 (2023).
3. Ferenc Segedin, Goodge et al., Nat. Comm. 14, 1468 (2023).
4. Lee et al., Nature 619, 288–292 (2023).
5. Ko et al., Nature 638, 935–940 (2025).
6. Bhatt et al., arXiv:2501.08204 (2025).
7. Goodge et al., Microsc. Microan. 26(3), 439–446 (2020).
8. Goodge & Kourkoutis. arXiv:2007.09747 (2020).
9. Goodge, Gonzalez et al., ACS Nano 17:20, 19865–19876 (2023).
10. Schnitzer et al., PRX 15, 011007 (2025).
Zoom link:
https://univienna.zoom.us/j/61659519193?pwd=bTL0tQGfJNs1bQ9r6hcH72eoFasO9e.1
28.10.2025
Roberto dos Reis (Northwestern University, USA)
Physics-informed machine learning for strain analysis in 4D STEM
Accurate strain field characterization from 4D Scanning Transmission Electron Microscopy (4D STEM) presents significant challenges when facing noisy and sparse datasets. Traditional strain analysis methods using peak detection and template matching fail when data quality is compromised or sampling is incomplete, limiting their applicability to real-world experimental conditions.
This presentation introduces a Physics-Informed Neural Network (PINN) framework that embeds fundamental elasticity equations directly into machine learning architectures. Our approach leverages automatic differentiation to enforce strain-displacement relationships, stress-strain constitutive laws, and mechanical equilibrium conditions as differentiable constraints during training. This physics-guided learning enables accurate strain field reconstruction from highly sparse and noisy 4D STEM datasets where conventional methods fail. Physics acts as a regularization mechanism, guiding neural networks toward physically meaningful solutions. When experimental data is sparse or corrupted, embedded elasticity constraints interpolate missing regions and suppress noise-induced artifacts.
We demonstrate improved accuracy in strain measurements compared to conventional methods while requiring fewer data points for reliable reconstruction. Case studies on semiconductor heterostructures and 2D materials show successful strain field extraction from challenging datasets with significant missing data and low signal-to-noise ratios. The framework enables robust analysis of beam-sensitive materials where high-dose imaging causes damage, providing quantitative uncertainty estimates while maintaining physical consistency across all measurements.
Zoom link:
https://univienna.zoom.us/j/61659519193?pwd=bTL0tQGfJNs1bQ9r6hcH72eoFasO9e.1
25.11.2025
Demie Kepaptsoglou (SuperSTEM, UK)
Electron Energy Loss Spectroscopy at high energy, spatial and momentum resolutions
Engineering the structural or chemical architecture of functional materials at the nano or even atomic level enables emergent properties that rely on the interplay between fundamental properties of matter such as charge, spin and local atomic-scale chemistry. Thanks to advances in monochromators, state-of-the-art electron energy loss spectroscopy (EELS) in the scanning transmission electron microscope (STEM), offers nowadays the ability to map materials and atomic structures with an angstrom size electron beam and an energy resolution for EELS under 5 meV [1]. These capabilities have allowed to probe the spectroscopic signature of phonons down to the single atom level [2] as well the detection of spin waves (magnons) in an electron microscope [3]. These capabilities in tandem with high precision imaging and other wealth of information available have allowed for exciting new experiments in the electron microscope that rival synchrotron light sources. Here, we will be discussing recent methodological developments in STEM-EELS, experimental geometries for momentum and spatially resolved experiments, and describing theoretical tools used to support experimental findings.
1. O.L. Krivanek et al., J. Phys. Conf. Ser. 522, 012023 (2014).
2. F.S. Hage et al., Science 367, 1124 LP (2020).
3. D. Kepaptsoglou et al., Nature 644, 83–88 (2025).
Zoom link:
https://univienna.zoom.us/j/61659519193?pwd=bTL0tQGfJNs1bQ9r6hcH72eoFasO9e.1
09.12.2025
Yang Yang (Pennsylvania State University, USA)
Imaging defects in metals under extreme environment
Defects, such as vacancies and stacking faults, play pivotal roles in tuning the properties and performance of materials. Similarly, local ordering, such as chemical short-range order (SRO), could significantly influence material characteristics, including yield strength and resistance to radiation damage. Recent advancements in electron microscopy, such as four-dimensional scanning transmission electron microscopy (4D-STEM) and in situ TEM techniques, have enabled unparalleled characterization of defects and local ordering at the nanoscale.
This presentation highlights our recent studies investigating the interactions of defects and local ordering with extreme environments, including corrosion in molten salts and fatigue deformation in metals. Additionally, we explore the ubiquitous presence of SRO in multi-principal element alloys (MPEAs) and its profound impact on mechanical behavior. These studies collectively underscore the transformative potential of advanced electron microscopy in unraveling the mechanisms that govern materials' performance under extreme conditions.
Zoom link:
https://univienna.zoom.us/j/61659519193?pwd=bTL0tQGfJNs1bQ9r6hcH72eoFasO9e.1
13.01.2026
Eric Stach (University of Pennsylvania, USA)
Automating STEM acquisition and analysis of carbon nanotubes
To be announced.
Zoom link:
https://univienna.zoom.us/j/61027123134?pwd=hZPmenswvBbLiYz9htyDmD0SzrLe6Q.1