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Spin-polarized two-dimensional electron gases (2DEGs) are of particular interest for functional oxide electronics applications. The redox-created 2DEG residing on the strontium titanate, SrTiO$_3$ (STO), side of a europium monoxide (EuO)/SrTiO$_3$ (001) interface is expected to be significantly spin-polarized due to the proximity to the strong ($7\,\mu_B/f.u.$) Heisenberg ferromagnet EuO. We apply magnetic circular dichroism in the angular distribution (MCDAD) of photoemitted electrons to investigate whether and how the induced spin polarization of the 2DEG depends on the dimensionality of the overlaying EuO layer. The experimental data are complemented by density functional theory calculations with Hubbard $U$ term (DFT+$U$). We show that the EuO/STO interfacial 2DEG is spin-polarized even for ultrathin EuO overlayers, starting at an EuO threshold thickness of only two monolayers. Additional EuO monolayers even increase the induced magnetic Ti moment and thus the spin polarization of the 2DEG. Our results and the potential to enhance the magnetic order of EuO by other proximity effects [1] indicate that the EuO/STO (001) interface is an ideal template for creating (multi-)functional spin-polarized 2DEGs for application in oxide electronics., Comment: 9 pages, 5 figures

Generalized Additive Models (GAMs) offer a balance between performance and interpretability in machine learning. The interpretability aspect of GAMs is expressed through shape plots, representing the model's decision-making process. However, the visual properties of these plots, e.g. number of kinks (number of local maxima and minima), can impact their complexity and the cognitive load imposed on the viewer, compromising interpretability. Our study, including 57 participants, investigates the relationship between the visual properties of GAM shape plots and cognitive load they induce. We quantify various visual properties of shape plots and evaluate their alignment with participants' perceived cognitive load, based on 144 plots. Our results indicate that the number of kinks metric is the most effective, explaining 86.4% of the variance in users' ratings. We develop a simple model based on number of kinks that provides a practical tool for predicting cognitive load, enabling the assessment of one aspect of GAM interpretability without direct user involvement., Comment: to be published in proceedings of the 58th Hawaii International Conference on System Sciences (HICSS)

Machine learned potentials are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees of freedom at a coarse-grained resolution. One of the main criticisms to machine learned potentials is that the energy inferred by the network is not as interpretable as in more traditional approaches where a simpler functional form is used. Here we address this problem by extending tools recently proposed in the nascent field of Explainable Artificial Intelligence (XAI) to coarse-grained potentials based on graph neural networks (GNN). We demonstrate the approach on three different coarse-grained systems including two fluids (methane and water) and the protein NTL9. On these examples, we show that the neural network potentials can be in practice decomposed in relevance contributions to different orders, that can be directly interpreted and provide physical insights on the systems of interest.

The control of electron spins in materials that are simultaneously ferromagnetic and insulating opens up a wealth of quantum phenomena in spin-based electronics. Thin films of europium oxide (EuO) are ideal for the generation and manipulation of spin-polarized states, but so far there are no experimental literature reports on the magnetic domain patterns for EuO. However, at these microscopic length scales, magnetic relaxation between the remanent and demagnetized states takes place in any spintronic device. This relaxation process involves displacements of magnetic domain walls and can therefore be strongly influenced by the film structure and thickness. Here we present an investigation of the temperature-dependent behavior of magnetic domains and hysteresis in bulk-like (25 nm) and ultrathin (3 nm) EuO films. Magneto-optical Kerr microscopy is used, a technique that is a valuable tool to explore microscopic features such as spin dynamics and magnetic domain walls. Significant Kerr rotation in EuO led to high-contrast magnetic domain images in thick films, facilitating observation of domain dynamics. The critical temperature (TC) and coercivity shows strong thickness-dependent variations. The analysis and comparison of hysteresis loops and domain imaging in EuO and EuO/Co reveal proximity effect-induced antiferromagnetic coupling of both layers. To elucidate the magnetization reversal dynamics in EuO, micromagnetic simulations using MuMax3 were performed below and above TC. This comprehensive approach aims to comprehend the impact of magnetism and magnetic proximity effect in EuO on the micromagnetic scale, potentially extending its magnetic ordering beyond TC.

We develop the notion of a locally homomorphic channel and prove an approximate equivalence between those and codes for computing functions. Further, we derive decomposition properties of locally homomorphic channels which we use to analyze and construct codes where two messages must be encoded independently. This leads to new results for identification and K-identification when all messages are sent over multiple-access channels, which yield surprising rate improvements compared to naive code constructions. In particular, we demonstrate that for the example of identification with deterministic encoders, both encoders can be constructed independently., Comment: 6 pages, 1 figure

Proximity effects allow for the adjustment of magnetic properties in a physically elegant way. If two thin ferromagnetic (FM) films are brought into contact, electronic coupling alters their magnetic exchange interaction at their interface. For a low-TC rare-earth FM coupled to a 3d transition metal FM, even room temperature magnetism is within reach. In addition, magnetic proximity coupling is particularly promising for increasing the magnetic order of metastable materials such as europium monoxide (EuO) beyond their bulk TC, since neither the stoichiometry nor the insulating properties are modified. We investigate the magnetic proximity effect at Fe/EuO and Co/EuO interfaces using hard X-ray photoelectron spectroscopy. By exciting the FM layers with circularly polarized light, magnetic dichroism is observed in angular dependence on the photoemission geometry. In this way, the depth-dependence of the magnetic signal is determined element-specifically for the EuO and 3d FM parts of the bilayers. In connection with atomistic spin dynamics simulations, the thickness of EuO layer is found to be crucial, indicating that the observed antiferromagnetic proximity coupling is a short-ranged and genuine interface phenomenon. This fact turns the bilayer into a strong synthetic ferrimagnet. The increase in magnetic order in EuO occurs in a finite spatial range and is therefore particularly strong in the 2D limit-a counterintuitive but very useful phenomenon for spin-based device applications.

To this date the safety assessment of materials, used for example in the nuclear power sector, commonly relies on a fracture mechanical analysis utilizing macroscopic concepts, where a global load quantity K or J is compared to the materials fracture toughness curve. Part of the experimental effort involved in these concepts is dedicated to the quantitative analysis of fracture surfaces. Within the scope of this study a methodology for the semi-supervised training of deep learning models for fracture surface segmentation on a macroscopic level was established. Therefore, three distinct and unique datasets were created to analyze the influence of structural similarity on the segmentation capability. The structural similarity differs due to the assessed materials and specimen, as well as imaging-induced variance due to fluctuations in image acquisition in different laboratories. The datasets correspond to typical isolated laboratory conditions, complex real-world circumstances, and a curated subset of the two. We implemented a weak-to-strong consistency regularization for semi-supervised learning. On the heterogeneous dataset we were able to train robust and well-generalizing models that learned feature representations from images across different domains without observing a significant drop in prediction quality. Furthermore, our approach reduced the number of labeled images required for training by a factor of 6. To demonstrate the success of our method and the benefit of our approach for the fracture mechanics assessment, we utilized the models for initial crack size measurements with the area average method. For the laboratory setting, the deep learning assisted measurements proved to have the same quality as manual measurements. For models trained on the heterogeneous dataset, very good measurement accuracies with mean deviations smaller than 1 % could be achieved..., Comment: During review title changed to: Deep learning based initial crack size measurements utilizing macroscale fracture surface segmentation

The photoelectric effect is not truly instantaneous, but exhibits attosecond delays that can reveal complex molecular dynamics. Sub-femtosecond duration light pulses provide the requisite tools to resolve the dynamics of photoionization. Accordingly, the past decade has produced a large volume of work on photoionization delays following single photon absorption of an extreme ultraviolet (XUV) photon. However, the measurement of time-resolved core-level photoionization remained out of reach. The required x-ray photon energies needed for core-level photoionization were not available with attosecond tabletop sources. We have now measured the x-ray photoemission delay of core-level electrons, and here report unexpectedly large delays, ranging up to 700 attoseconds in NO near the oxygen K-shell threshold. These measurements exploit attosecond soft x-ray pulses from a free-electron laser (XFEL) to scan across the entire region near the K-shell threshold. Furthermore, we find the delay spectrum is richly modulated, suggesting several contributions including transient trapping of the photoelectron due to shape resonances, collisions with the Auger-Meitner electron that is emitted in the rapid non-radiative relaxation of the molecule, and multi-electron scattering effects. The results demonstrate how x-ray attosecond experiments, supported by comprehensive theoretical modelling, can unravel the complex correlated dynamics of core-level photoionization.

Surface charges play a fundamental role in physics and chemistry, particularly in shaping the catalytic properties of nanomaterials. Tracking nanoscale surface charge dynamics remains challenging due to the involved length and time scales. Here, we demonstrate real-time access to the nanoscale charge dynamics on dielectric nanoparticles employing reaction nanoscopy. We present a four-dimensional visualization of the non-linear charge dynamics on strong-field irradiated single SiO$_2$ nanoparticles with femtosecond-nanometer resolution and reveal how surface charges affect surface molecular bonding with quantum dynamical simulations. We performed semi-classical simulations to uncover the roles of diffusion and charge loss in the surface charge redistribution process. Understanding nanoscale surface charge dynamics and its influence on chemical bonding on a single nanoparticle level unlocks an increased ability to address global needs in renewable energy and advanced healthcare., Comment: 26 pages with (4+6(SI)) figures

Oxide electronics provide the key concepts and materials for enhancing silicon-based semiconductor technologies with novel functionalities. However, a basic but key property of semiconductor devices still needs to be unveiled in its oxidic counterparts: the ability to set or even switch between two types of carriers - either negatively (n) charged electrons or positively (p) charged holes. Here, we provide direct evidence for individually emerging n- or p-type 2D band dispersions in STO-based heterostructures using resonant photoelectron spectroscopy. The key to tuning the carrier character is the oxidation state of an adjacent Fe-based interface layer: For Fe and FeO, hole bands emerge in the empty band gap region of STO due to hybridization of Ti and Fe-derived states across the interface, while for Fe$_3$O$_4$ overlayers, an 2D electron system is formed. Unexpected oxygen vacancy characteristics arise for the hole-type interfaces, which as of yet had been exclusively assigned to the emergence of 2DESs. In general, this finding opens up the possibility to straightforwardly switch the type of conductivity at STO interfaces by the oxidation state of a redox overlayer. This will extend the spectrum of phenomena in oxide electronics, including the realization of combined n/p-type all-oxide transistors or logic gates., Comment: Advanced Materials (accepted)

Atomic-scale defect detection is shown in scanning tunneling microscopy images of single crystal WSe2 using an ensemble of U-Net-like convolutional neural networks. Standard deep learning test metrics indicated good detection performance with an average F1 score of 0.66 and demonstrated ensemble generalization to C-AFM images of WSe2 and STM images of MoSe2. Defect coordinates were automatically extracted from defect detections maps showing that STM image analysis enhanced by machine learning can be used to dramatically increase sample characterization throughput., Comment: 9 pages, 4 figures, submitted to MRS Advances as a conference preceding for the 2023 MRS Fall Meeting & Exhibit

The determination of the surface free energy $\gamma_{\text{SV}}$ of solid materials provides information to explain and understand a wide variety of phenomena in surface science. One of the most widely used methods to determine the value of $\gamma_{\text{SV}}$ is the Neumann equation of state (EQS), whose application requires the values of the contact angle $(\theta)$ and the surface tension of a probe liquid $\gamma_{\text{LV}}$. Both parameters are determined experimentally, and their values may be subject to considerable uncertainty. In the present work, the uncertainties of the EQS are analyzed by means of the Taylor series method, using a polynomial adjustment to the EQS previously reported. This analysis allowed us to determine the effects of the ${\theta}$ and $\gamma_{\text{LV}}$ uncertainties on $\gamma_{\text{SV}}$ values. The $\gamma_{\text{SV}}$ of polyoxymethylene (POM) and polytetrafluoroethylene (PTFE) was calculated using the EQS from four probe liquids and taking into account the propagation of the ${\theta}$ and $\gamma_{\text{LV}}$ uncertainties. Generally, the comparison of the $\gamma_{\text{SV}}$ values calculated from the different probe fluids revealed significant differences. As a solution to this inconsistency in the EQS method, we proposed taking into account a systematic standard uncertainty associated with the method equal to $1.3 \, \text{mJ/m}^2$ . This allowed the differences between the $\gamma_{\text{SV}}$ values for the different probe liquids to be non-significant and therefore the method to be consistent., Comment: 46 pages, 6 tables, 10 figures

The present work presents a stable POD-Galerkin based reduced-order model (ROM) for two-dimensional Rayleigh-B\'enard convection in a square geometry for three Rayleigh numbers: $10^4$ (steady state), $3\times 10^5$ (periodic), and $6 \times 10^6$ (chaotic). Stability is obtained through a particular (staggered-grid) full-order model (FOM) discretization that leads to a ROM that is pressure-free and has skew-symmetric (energy-conserving) convective terms. This yields long-time stable solutions without requiring stabilizing mechanisms, even outside the training data range. The ROM's stability is validated for the different test cases by investigating the Nusselt and Reynolds number time series and the mean and variance of the vertical temperature profile. In general, these quantities converge to the FOM when increasing the number of modes, and turn out to be a good measure of accuracy. However, for the chaotic case, convergence with increasing numbers of modes is relatively difficult and a high number of modes is required to resolve the low-energy structures that are important for the global dynamics.

An upper bound to the identification capacity of discrete memoryless wiretap channels is derived under the requirement of semantic effective secrecy, combining semantic secrecy and stealth constraints. A previously established lower bound is improved by applying it to a prefix channel, formed by concatenating an auxiliary channel and the actual channel. The bounds are tight if the legitimate channel is more capable than the eavesdropper's channel. An illustrative example is provided for a wiretap channel that is composed of a point-to-point channel, and a parallel, reversely degraded wiretap channel. A comparison with results for message transmission and for identification with only secrecy constraint is provided., Comment: ISIT 2023 version

Deterministic identification over K-input multiple-access channels with average input cost constraints is considered. The capacity region for deterministic identification is determined for an average-error criterion, where arbitrarily large codes are achievable. For a maximal-error criterion, upper and lower bounds on the capacity region are derived. The bounds coincide if all average partial point-to-point channels are injective under the input constraint, i.e. all inputs at one terminal are mapped to distinct output distributions, if averaged over the inputs at all other terminals. The achievability is proved by treating the MAC as an arbitrarily varying channel with average state constraints. For injective average channels, the capacity region is a hyperrectangle. The modulo-2 and modulo-3 binary adder MAC are presented as examples of channels which are injective under suitable input constraints. The binary multiplier MAC is presented as an example of a non-injective channel, where the achievable identification rate region still includes the Shannon capacity region., Comment: ISIT 2023 version