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Grain boundaries (GBs) trigger structure-specific chemical segregation of solute atoms. According to the three-dimensional (3D) topology of grains, GBs - although defined as planar defects - cannot be free of curvature. This implies formation of topologically-necessary arrays of secondary GB dislocations. We report here that these pattern-forming secondary GB dislocations can have an additional and, in some cases, even a much stronger effect on GB segregation than defect-free GBs. Using nanoscale correlative tomography combining crystallography and chemical analysis, we quantified the relationship between secondary GB dislocations and their segregation energy spectra for a model Fe-W alloy. This discovery unlocks new design opportunities for advanced materials, leveraging the additional degrees of freedom provided by topologically-necessary secondary GB dislocations to modulate segregation.

The question of how to best serve the student populations of our country is a complex topic. Since public funding is limited, we must explore the best ways to direct the money to improve student outcomes. Previous research has suggested that socio-economic status is the best predictor of student achievement, while other studies suggest that the amount of money spent on the student is a more significant factor. In this paper, we explore this question and its impacts on Maryland, Virginia, and the District of Columbia schools. We conclude that the graduation rate has a direct relationship with unemployment, suggesting that funding towards improving out-of-school opportunities and quality of life will significantly improve students chances of success. We do not find a significant relationship between per-pupil spending and student achievement., Comment: https://github.com/araabe2/Data-601-Group-Project

Due to significant variations in the projection of the same object from different viewpoints, machine learning algorithms struggle to recognize the same object across various perspectives. In contrast, toddlers quickly learn to recognize objects from different viewpoints with almost no supervision. Recent works argue that toddlers develop this ability by mapping close-in-time visual inputs to similar representations while interacting with objects. High acuity vision is only available in the central visual field, which may explain why toddlers (much like adults) constantly move their gaze around during such interactions. It is unclear whether/how much toddlers curate their visual experience through these eye movements to support learning object representations. In this work, we explore whether a bio inspired visual learning model can harness toddlers' gaze behavior during a play session to develop view-invariant object recognition. Exploiting head-mounted eye tracking during dyadic play, we simulate toddlers' central visual field experience by cropping image regions centered on the gaze location. This visual stream feeds a time-based self-supervised learning algorithm. Our experiments demonstrate that toddlers' gaze strategy supports the learning of invariant object representations. Our analysis also reveals that the limited size of the central visual field where acuity is high is crucial for this. We further find that toddlers' visual experience elicits more robust representations compared to adults' mostly because toddlers look at objects they hold themselves for longer bouts. Overall, our work reveals how toddlers' gaze behavior supports self-supervised learning of view-invariant object recognition., Comment: 16 pages, 11 figures

Hydrogen, while a promising sustainable energy carrier, presents challenges such as the embrittlement of materials due to its ability to penetrate and weaken their crystal structures. Here we investigate Fe4N nitride layers, formed on iron through a cost-effective gas nitriding process, as an effective hydrogen permeation barrier. A combination of screening using advanced characterization, density functional theory calculations, and hydrogen permeation analysis reveals that a nitride layer reduces hydrogen diffusion by a factor of 20 at room temperature. This reduction is achieved by creating energetically unfavorable states due to stronger H-binding at the surface and high energy barriers for diffusion. The findings demonstrate the potential of Fe4N as a cost-efficient and easy-to-process solution to protecting metallic materials exposed to hydrogen, with great advantages for large-scale applications.

High-entropy alloys (HEAs) are metallic materials with solid solutions stabilized by high mixing entropy. Some exhibit excellent strength, often accompanied by additional properties such as magnetic, invar, corrosion, or cryogenic response. This has spurred efforts to discover new HEAs, but the vast compositional search space has made these efforts challenging. Here we present a framework to predict and optimize the yield strength of face-centered cubic (FCC) HEAs, using CoCrFeMnNi-based alloys as a case study due to abundant available data. Our novel Residual Hybrid Learning Model (RELM) integrates Random Forest and Gradient Boosting, enhanced by material attribute data, to handle sparse, skewed datasets for real-world alloys. A hybrid Generative Adversarial Network-Variational Autoencoder model explores new alloy compositions beyond existing datasets. By incorporating processing parameters, which determine the microstructure and thus strength, RELM achieves an R$^2$ score of 0.915, surpassing traditional models. SHapley Additive Explanations (SHAP) and Partial Dependencies enhance interpretability, revealing composition-processing-property relationships, as validated by experiments, including X-ray diffraction, SEM analysis, and tensile testing. The model discovered two novel Co$_{20}$Cr$_{16}$Fe$_{20}$Mn$_{16}$Ni$_{24}$Al$_4$ and Co$_{24}$Cr$_{12}$Fe$_{12}$Mn$_{16}$Ni$_{28}$Al$_4$Si$_4$ HEAs with a maximum possible yield strength of 842 and 937 MPa, significantly exceeding previously reported values for these alloy systems. This study pioneers interpretable machine learning in alloy design, providing a rigorous, data-driven approach to discovering, processing, and optimizing real-world materials. The findings highlight the critical role of both compositional and post-fabrication processing parameters in advancing the understanding of composition-processing-property relationships.

The purpose of the current work is the development and comparison of Fourier neural operators (FNOs) for surrogate modeling of the quasi-static mechanical response of polycrystalline materials. Three types of such FNOs are considered here: a physics-guided FNO (PgFNO), a physics-informed FNO (PiFNO), and a physics-encoded FNO (PeFNO). These are trained and compared with the help of stress field data from a reference model for heterogeneous elastic materials with a periodic grain microstructure. Whereas PgFNO training is based solely on these data, that of the PiFNO and PeFNO is in addition constrained by the requirement that stress fields satisfy mechanical equilibrium, i.e., be divergence-free. The difference between the PiFNO and PeFNO lies in how this constraint is taken into account; in the PiFNO, it is included in the loss function, whereas in the PeFNO, it is "encoded" in the operator architecture. In the current work, this encoding is based on a stress potential and Fourier transforms. As a result, only the training of the PiFNO is constrained by mechanical equilibrium; in contrast, mechanical equilibrium constrains both the training and output of the PeFNO. Due in particular to this, stress fields calculated by the trained PeFNO are significantly more accurate than those calculated by the trained PiFNO in the example cases considered., Comment: 17 pages, 11 figures

The dynamics of domain walls in a square of permalloy (Ni$_{81}$Fe$_{19}$; Py) upon excitation with an oscillating magnetic field of 4 mT amplitude were recorded by pump-probe ptychography with X-ray magnetic circular dichroism (XMCD) at the Ni L$_3$-edge. The 2.5 $\mu$m Py square of 160 nm thickness forms a vortex flux-closure pattern with domain walls that fall into alternating out-of-plane magnetization states due to the interplay of in-plane shape and growth-induced perpendicular anisotropies. Dynamic modes of the domain wall structure were excitable along with the vortex core gyration with frequencies of 500 MHz and 1 GHz. Micromagnetic simulations served to corroborate the imaged domain wall motion.

The ferroelectric domain structure of a freestanding BiFeO$_3$ film was visualized by ptychographic dichroic imaging with linearly polarized X-rays at the O K-edge around 530 eV. The dichroic contrast is maximized at the energy of the hybridization of the O 2p state and the Fe 3d orbitals, which is split by the octahedral crystal field of the perovskite structure. The thus obtained microscopy images compliment the ptychographic imaging of the antiferromagnetic contribution at the Fe L$_3$-edge. The approach is extendible to the separation of different ferroic contributions in other multiferroic oxides.

Synthesizing distinct phases and controlling the crystalline defects in them are key concepts in materials and process design. These approaches are usually described by decoupled theories, with the former resting on equilibrium thermodynamics and the latter on nonequilibrium kinetics. By combining them into a holistic form of defect phase diagrams, we can apply phase equilibrium models to the thermodynamic evaluation of defects such as vacancies, dislocations, surfaces, grain boundaries, and phase boundaries, placing the understanding of material imperfections and their role on properties on solid thermodynamic and theoretical grounds. In this study, we characterize an interface-stabilized phase between Fe and Fe3O4 (magnetite) with differential phase contrast (DPC) imaging in scanning transmission electron microscopy (STEM). This method uniquely enables the simultaneous imaging of both heavy Fe atoms and light O atoms, providing precise mapping of the atomic structure and chemical composition at this heterogeneous metal-oxide interface. We identify a well-ordered two-layer interface-stabilized phase state (referred to as complexion) at the Fe[001]/Fe3O4[001] interface. Using density-functional theory (DFT), we not only explain the observed complexion but also map out various interface-stabilized phases as a function of the O chemical potential. We show that the formation of complexions influences the properties of the interface, increasing its adhesion by 20 % and changing the charge transfer between adjacent materials, also leveraging impact on the transport properties across such interfaces. Our findings highlight the potential of tunable phase states at defects as a new asset in advanced materials design, paving the way for knowledge-based and optimized corrosion protection, catalysis, magnetism, and redox-driven phase transitions.

The combined structural and electronic complexity of iron oxides poses many challenges to atomistic modeling. To leverage limitations in terms of the accessible length and time scales, one requires a physically justified interatomic potential which is accurate to correctly account for the complexity of iron-oxygen systems. Such a potential is not yet available in the literature. In this work, we propose a machine-learning potential based on the Atomic Cluster Expansion for modeling the iron-oxygen system, which explicitly accounts for magnetism. We test the potential on a wide range of properties of iron and its oxides, and demonstrate its ability to describe the thermodynamics of systems spanning the whole range of oxygen content and including magnetic degrees of freedom., Comment: Manuscript: 14 pages, 9 figures, SI: 6 pages, 7 figures

We study the dynamical behavior of medium-size multimode VCSELs with an elliptical oxide aperture, selected for the best trade-off between high output power and modulation speed for datacom applications, with a focus on their relative intensity noise (RIN) performance. Our experimental results, collected for various VCSELs, outline the presence of several peaks in the RIN spectra within the bandwidth of the transmission system, which can limit the eye opening under direct current modulation. Here, we present a rigorous model to explain for the first time the origin of these peaks. In particular, the frequencies of the spectral RIN peaks are analytically described as the result of the non-trivial interaction among transverse modes by addressing the laser dynamics and the related noise features through a time-domain mode expansion approach, accounting for coherent effects in multimode competition, spatial hole burning, and carrier diffusion. The laser modulation performance is addressed through dynamical simulations with PAM2 and PAM4 modulations, which clearly demonstrate the potential for high-bitrate optical interconnects. Finally, we address the effect of the oxide aperture aspect ratio via electromagnetic simulations, demonstrating how the ellipticity affects the modal frequency detuning and the RIN, thus providing design guidelines for VCSELs with low RIN performance and outlining a clear roadmap for a substantially improved bandwidth-power trade-off in these devices., Comment: 12 pages, 8 figures

The development of sustainable transportation and communication systems requires an increase in both energy density and capacity retention of Li-batteries. Using substrates forming a solid solution with body centered cubic Li enhances the cycle stability of anode-less batteries. However, it remains unclear how the substrate microstructure affects the lithiation behavior. Here, we deploy a correlative, near-atomic scale probing approach through combined ion- and electron-microscopy to examine the distribution of Li in Li-Ag diffusion couples as model system. We reveal that Li regions with over 93.8% at.% nucleate within Ag at random high angle grain boundaries, whereas grain interiors are not lithiated. We evidence the role of kinetics and mechanical constraint from the microstructure over equilibrium thermodynamics in dictating the lithiation process. The findings suggest that grain size and grain boundary character are critical to enhance the electrochemical performance of interlayers/electrodes, particularly for improving lithiation kinetics and hence reducing dendrite formation.

The interaction between boron and hydrogen at grain boundaries has been investigated experimentally and numerically in boron-doped and boron-free martensitic steels using thermal desorption spectrometry (TDS) and ab initio calculations. The calculations show that boron and hydrogen are attracted to grain boundaries but boron can repel hydrogen. This behavior has also been observed using TDS measurements, with the disappearance of one peak when boron is incorporated into the microstructure. Additionally, the microstructure of both steels has been studied through electron backscattered diffraction, electron channeling contrast imaging, synchrotron X-ray measurements, and atom probe tomography. While they have a similar grain size, grain boundary distribution, and dislocation densities, a pronounced boron segregation into PAGBs is observed for boron-doped steels. Then, the equilibrium hydrogen concentration in different trapping sites has been evaluated using the Langmuir-McLean approximation. This thermodynamic model shows that the distribution of hydrogen is identical for all traps when the total hydrogen concentration is low for boron-free steel. However, when it increases, traps of the lowest segregation energies (mostly PAGBs) are firstly saturated, which promotes failure initiation at this defect type. This finding partially explains why PAGBs are the weakest microstructure feature when martensitic steels are exposed to hydrogen-containing environments., Comment: Pre-printed version

Robotic assistance has significantly improved the outcomes of open microsurgery and rigid endoscopic surgery, however is yet to make an impact in flexible endoscopic neurosurgery. Some of the most common intracranial procedures for treatment of hydrocephalus and tumors stand to benefit from increased dexterity and reduced invasiveness offered by robotic systems that can navigate in the deep ventricular system of the brain. We review a spectrum of flexible robotic devices, from the traditional highly actuated approach, to more novel and bio-inspired mechanisms for safe navigation. For each technology, we identify the operating principle and are able to evaluate the potential for minimally invasive surgical applications. Overall, rigid-type continuum robots have seen the most development, however, approaches combining rigid and soft robotic principles into innovative devices, are ideally situated to address safety and complexity limitations after future design evolution. We also observe a number of related challenges in the field, from surgeon-robot interfaces to robot evaluation procedures. Fundamentally, the challenges revolve around a guarantee of safety in robotic devices with the prerequisites to assist and improve upon surgical tasks. With innovative designs, materials and evaluation techniques emerging, we see potential impacts in the next 5--10 years., Comment: 12 pages, 10 figures

Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research., Comment: Review, outlook, roadmap, perspective

Steady technological advances and recent milestones such as intercontinental quantum communication and the first implementation of medium-scale quantum networks are paving the way for the establishment of the quantum internet, a network of nodes interconnected by quantum channels. Here we build upon recent models for quantum networks based on optical fibers by considering the effect of a non-uniform distribution of nodes, more specifically based on the demographic data of the federal states in Brazil. We not only compute the statistical properties of this more realistic network, comparing its features with previous models but also employ it to compute the repetition rates for entanglement swapping, an essential protocol for quantum communication based on quantum repeaters., Comment: 14 pages, 8 figures and 1 table

Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the human mind and may facilitate the construction of artificial minds with similar properties. Importantly, human cognitive development relies on embodied interactions with the physical and social environment, which is perceived via complementary sensory modalities. These interactions allow the developing mind to probe the causal structure of the world. This is in stark contrast to common machine learning approaches, e.g., for large language models, which are merely passively ``digesting'' large amounts of training data, but are not in control of their sensory inputs. However, computational modeling of the kind of self-determined embodied interactions that lead to human intelligence and consciousness is a formidable challenge. Here we present MIMo, an open-source multi-modal infant model for studying early cognitive development through computer simulations. MIMo's body is modeled after an 18-month-old child with detailed five-fingered hands. MIMo perceives its surroundings via binocular vision, a vestibular system, proprioception, and touch perception through a full-body virtual skin, while two different actuation models allow control of his body. We describe the design and interfaces of MIMo and provide examples illustrating its use. All code is available at https://github.com/trieschlab/MIMo ., Comment: 11 pages, 8 figures. Submitted to IEEE Transactions on Congnitive and Developmental Systems (TCDS)

When H, the lightest, smallest and most abundant atom in the universe, makes its way into a high-strength alloy (>650 MPa), the material's load-bearing capacity is abruptly lost. This phenomenon, known as H embrittlement, was responsible for the catastrophic and unpredictable failure of large engineering structures in service. The inherent antagonism between high strength requirements and H embrittlement susceptibility strongly hinders the design of lightweight yet reliable structural components needed for carbon-free hydrogen-propelled industries and reduced-emission transportation solutions. Inexpensive and scalable alloying and microstructural solutions that enable both, an intrinsically high resilience to H and high mechanical performance, must be found. Here we introduce a counterintuitive strategy to exploit typically undesired chemical heterogeneity within the material's microstructure that allows the local enhancement of crack resistance and local H trapping, thereby enhancing the resistance against H embrittlement. We deploy this approach to a lightweight, high-strength steel and produce a high-number density Mn-rich zones dispersed within the microstructure. These solute-rich buffer regions allow for local micro-tuning of the phase stability, arresting H-induced microcracks thus interrupting the H-assisted damage evolution chain, regardless of how and when H is introduced and also regardless of the underlying embrittling mechanisms. A superior H embrittlement resistance, increased by a factor of two compared to a reference material with a homogeneous solute distribution within each microstructure constituent, is achieved at no expense of the material's strength and ductility.

Understanding the magnetic and ferroelectric ordering of magnetoelectric multiferroic materials at the nanoscale necessitates a versatile imaging method with high spatial resolution. Here, soft X-ray ptychography is employed to simultaneously image the ferroelectric and antiferromagnetic domains in an 80 nm thin freestanding film of the room-temperature multiferroic BiFeO$_3$ (BFO). The antiferromagnetic spin cycloid of period 64 nm is resolved by reconstructing the corresponding resonant elastic X-ray scattering in real space and visualized together with mosaic-like ferroelectric domains in a linear dichroic contrast image at the Fe L$_3$ edge. The measurements reveal a near perfect coupling between the antiferromagnetic and ferroelectric ordering by which the propagation direction of the spin cycloid is locked orthogonally to the ferroelectric polarization. In addition, the study evinces both a preference for in-plane propagation of the spin cycloid and changes of the ferroelectric polarization by 71{\deg} between multiferroic domains in the epitaxial strain-free, freestanding BFO film. The results provide a direct visualization of the strong magnetoelectric coupling in BFO and of its fine multiferroic domain structure, emphasizing the potential of ptychographic imaging for the study of multiferroics and non-collinear magnetic materials with soft X-rays., Comment: Supporting information available with published version: https://doi.org/10.1002/adma.202311157

This research investigates the growing trend of retail investors participating in certain stocks by organizing themselves on social media platforms, particularly Reddit. Previous studies have highlighted a notable association between Reddit activity and the volatility of affected stocks. This study seeks to expand the analysis to Twitter, which is among the most impactful social media platforms. To achieve this, we collected relevant tweets and analyzed their sentiment to explore the correlation between Twitter activity, sentiment, and stock volatility. The results reveal a significant relationship between Twitter activity and stock volatility but a weak link between tweet sentiment and stock performance. In general, Twitter activity and sentiment appear to play a less critical role in these events than Reddit activity. These findings offer new theoretical insights into the impact of social media platforms on stock market dynamics, and they may practically assist investors and regulators in comprehending these phenomena better.

Spin waves are collective perturbations in the orientation of the magnetic moments in magnetically ordered materials. Their rich phenomenology is intrinsically three-dimensional; however, the three-dimensional imaging of spin waves has so far not been possible. Here, we image the three-dimensional dynamics of spin waves excited in a synthetic antiferromagnet, with nanoscale spatial resolution and sub-ns temporal resolution, using time-resolved magnetic laminography. In this way, we map the distribution of the spin-wave modes throughout the volume of the structure, revealing unexpected depth-dependent profiles originating from the interlayer dipolar interaction. We experimentally demonstrate the existence of complex three-dimensional interference patterns and analyze them via micromagnetic modelling. We find that these patterns are generated by the superposition of spin waves with non-uniform amplitude profiles, and that their features can be controlled by tuning the composition and structure of the magnetic system. Our results open unforeseen possibilities for the study and manipulation of complex spin-wave modes within nanostructures and magnonic devices., Comment: 18 pages, 4 figures

Magnetic skyrmions have so far been treated as two-dimensional spin structures characterized by a topological winding number describing the rotation of spins across the skyrmion. However, in real systems with a finite thickness of the material being larger than the magnetic exchange length, the skyrmion spin texture extends into the third dimension and cannot be assumed as homogeneous. Using soft x-ray laminography we reconstruct with about 20nm spatial (voxel) resolution the full three-dimensional spin texture of a skyrmion in an 800 nm diameter and 95 nm thin disk patterned into a trilayer [Ir/Co/Pt] thin film structure. A quantitative analysis finds that the evolution of the radial profile of the topological skyrmion number and the chirality is non-uniform across the thickness of the disk. Estimates of local micromagnetic energy densities suggest that the changes in topological profile are related to non-uniform competing energetic interactions. Theoretical calculations and micromagnetic simulations are consistent with the experimental findings. Our results provide the foundation for nanoscale magnetic metrology for future tailored spintronics devices using topology as a design parameter, and have the potential to reverse-engineer a spin Hamiltonian from macroscopic data, tying theory more closely to experiment., Comment: 18 pages, 4 figures