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Gaussian Processes face two primary challenges: constructing models for large datasets and selecting the optimal model. This master's thesis tackles these challenges in the low-dimensional case. We examine recent convergence results to identify models with optimal convergence rates and pinpoint essential parameters. Utilizing this model, we propose a Samplet-based approach to efficiently construct and train the Gaussian Processes, reducing the cubic computational complexity to a log-linear scale. This method facilitates optimal regression while maintaining efficient performance.

Ab initio techniques have revolutionized the way how theory can help practitioners to explore critical mechanisms and devise new strategies in discovering and designing materials. Yet, their application to electrochemical systems is still limited. A well known example how novel concepts can boost our ability to perform such studies is the introduction of temperature control into ab initio simulations. The analogous technique to model electrochemical systems -- potential control -- is just emerging. In this review, we critically discuss state-of-the-art approaches to describe electrified interfaces between a solid electrode and a liquid electrolyte in realistic environments. By exchanging energy, electronic charge and ions with their environment, electrochemical interfaces are thermodynamically open systems. In addition, on the time and length scales relevant for chemical reactions, large fluctuations of the electrostatic potential and field occur. We systematically discuss the key challenges in including these features in realistic ab initio simulations, as well as the available techniques and approaches to overcome them, in order to facilitate the development and use of these novel techniques to the wider community. These methodological developments provide researchers with a new level of realism to explore fundamental electrochemical mechanisms and reactions from first principles.

We propose an analytical thermodynamic model for describing defect phase transformations, which we term the statistical phase evaluation approach (SPEA). The SPEA model assumes a Boltzmann distribution of finite size phase fractions and calculates their statistical average. To benchmark the performance of the model, we apply it to construct binary surface phase diagrams of metal alloys. Two alloy systems are considered: a Mg surface with Ca substitutions and a Ni surface with Nb substitutions. To construct a firm basis against which the performance of the analytical model can be leveled, we first perform Monte Carlo (MC) simulations coupled with cluster expansion of density functional theory dataset. We then demonstrate the SPEA model to reproduce the MC results accurately. Specifically, it correctly predicts the surface order-disorder transitions as well as the coexistence of the 1/3 ordered phase and the disordered phase. Finally, we compare the SPEA method to the sublattice model commonly used in the CALPHAD approach to describe ordered and random solution phases and their transitions. The proposed SPEA model provides a highly efficient approach for modeling defect phase transformations., Comment: Main: 10 pagers, 9 figures, SI: 2 pages, 5 figures

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the "fourth industrial revolution", referred to as "Industrie 4.0". Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements. This paper proposes representative use cases, which have been examined in the German Tactile Internet 4.0 (TACNET 4.0) research project. In order to analyze these use cases, this paper identifies the main challenges and requirements of communication networks in Industrie 4.0 and discusses the applicability of 5th generation wireless communication systems (5G).

Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting temperatures. Complementary theoretical predictions are, therefore, indispensable. The conventional free energy approach using density functional theory (DFT) has been a gold standard for such purposes because of its high accuracy. However,it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations. The high computational cost makes high-throughput calculations infeasible. Here, we propose a highly efficient DFT-based method aided by a specially designed machine learning potential. As the machine learning potential can closely reproduce the ab initio phase space, even for multi-component alloys, the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations. The method achieves overall savings of computational resources by 80% compared to current alternatives. We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties, including melting temperature, entropy and enthalpy of fusion, and volume change at the melting point. Additionally, the heat capacities of solid and liquid TaVCrW are calculated. The results agree reasonably with the calphad extrapolated values., Comment: 14 pages, 6 figures

Single-molecule magnets (SMMs) with chemically tailorable properties are potential building blocks for quantum computing, high-density magnetic memory, and spintronics.1 2 3,4 These applications require isolated or few molecules on substrates, but studies of SMMs have mainly focused on bulk crystals. Moreover, fabrication of SMM-based devices and electrical detection of the SMM magnetic state are still coveted milestones that have so far been achieved mainly for double-decker rare-earth phthalocyanines at temperatures below 1 K.5-8 Here we demonstrate electrical detection of magnetization switching for a modification of the archetypal SMM Mn12, up to 70 K, based on the supramolecular spin valve effect5 with graphene quantum dots9. Notably, the exchange interaction between the molecules and the graphene, as well as the dot-mediated intermolecular interaction, can be directly extracted from the electrical response, opening the way to an effective characterization of the quantum properties of different types of SMMs in a wide temperature range.

Zener ordering is a phenomenon that octahedral interstitial atoms such as carbon occupy the same sublattice inside bcc matrix such as iron. The original formulation relies on a mean field theory, which is still most in use today. We employ multiple methods, such as Molecular Dynamics, Metropolis Monte Carlo, Mean Field Theory with chemical interactions and finite temperature effects to show that the Zener ordering for iron carbon systems is governed by local chemical interactions and finite temperature effects and less of mean field nature as described originally by Zener.

Aqueous metal corrosion is a major economic concern in modern society. A phenomenon that has puzzled generations of scientists in this field is the so-called anomalous hydrogen evolution: the violent dissolution of magnesium under electron-rich (anodic) conditions, accompanied by strong hydrogen evolution, and a key mechanism hampering Mg technology. Experimental studies have indicated the presence of univalent Mg$^+$ in solution, but these findings have been largely ignored because they defy our common chemical understanding and evaded direct experimental observation. Using recent advances in the \emph{ab initio} description of solid-liquid electrochemical interfaces under controlled potential conditions, we described the full reaction path of Mg atom dissolution from a kinked Mg surface under anodic conditions. Our study reveals the formation of a solvated [Mg$^{2+}$(OH)$^-$]$^+$ ion complex, challenging the conventional assumption of Mg$^{2+}$ ion. This insight provides an intuitive explanation for the postulated presence of (coulombically) univalent Mg$^+$ ions and the absence of protective oxide/hydroxide layers normally formed under anodic/oxidizing conditions. The discovery of this unexpected and unconventional reaction mechanism is crucial for identifying new strategies for corrosion prevention and can be transferred to other metals.

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

The introduction and regulation of loudness in broadcasting and streaming brought clear benefits to the audience, e.g., a level of uniformity across programs and channels. Yet, speech loudness is frequently reported as being too low in certain passages, which can hinder the full understanding and enjoyment of movies and TV programs. This paper proposes expanding the set of loudness-based measures typically used in the industry. We focus on speech loudness, and we show that, when clean speech is not available, Deep Neural Networks (DNNs) can be used to isolate the speech signal and so to accurately estimate speech loudness, providing a more precise estimate compared to speech-gated loudness. Moreover, we define critical passages, i.e., passages in which speech is likely to be hard to understand. Critical passages are defined based on the local Speech Loudness Deviation (SLD) and the local Speech-to-Background Loudness Difference (SBLD), as SLD and SBLD significantly contribute to intelligibility and listening effort. In contrast to other more comprehensive measures of intelligibility and listening effort, SLD and SBLD can be straightforwardly measured, are intuitive, and, most importantly, can be easily controlled by adjusting the speech level in the mix or by enabling personalization at the user's end. Finally, examples are provided that show how the detection of critical passages can support the evaluation and control of the speech signal during and after content production., Comment: Accepted for presentation at the Audio Engineering Society (AES) 156th Convention, June 2024, Madrid, Spain

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.

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

Field evaporation from ionic or covalently bonded materials often leads to the emission of molecular ions. The metastability of these molecular ions, particularly under the influence of the intense electrostatic field (1010 Vm-1), makes them prone to dissociation with or without an exchange of energy amongst them. These processes can affect the analytical performance of atom probe tomography (APT). For instance, neutral species formed through dissociation may not be detected at all or with a time of flight no longer related to their mass, causing their loss from the analysis. Here, we evaluated the changes in the measured composition of FeO, Fe2O3 and Fe3O4 across a wide range of analysis conditions. Possible dissociation reactions are predicted by density-functional theory (DFT) calculations considering the spin states of the molecules. The energetically favoured reactions are traced on to the multi-hit ion correlation histograms, to confirm their existence within experiments, using an automated Python-based routine. The detected reactions are carefully analysed to reflect upon the influence of these neutrals from dissociation reactions on the performance of APT for analysing iron oxides.

We present an implementation of Triplet Excitation-Energy Transfer (TEET) couplings based on subsystem-based Time-Dependent Density-Functional Theory (sTDDFT). TEET couplings are systematically investigated by comparing "exact" and approximate variants of sTDDFT. We demonstrate that, while sTDDFT utilizing explicit approximate Non-Additive Kinetic Energy (NAKE) density functionals is well-suited for describing Singlet Excitation-Energy Transfer (SEET) processes, it is inadequate for characterizing TEET. However, we show that Projection-based Embedding (PbE)-based sTDDFT addresses the challenges faced by NAKE-sTDDFT and emerges as a promising method for accurately describing electronic couplings in TEET processes. We also introduce the mixed PbE-/NAKE-embedding procedure to investigate TEET effects in solvated pairs of chromophores. This approach offers a good balance between accuracy and efficiency, enabling comprehensive studies of TEET processes in complex environments.

Purpose: In this work, we explore the potential and limitations of simulating gaze-contingent tunnel vision conditions using Virtual Reality (VR) with built-in eye tracking technology. This approach promises an easy and accessible way of expanding study populations and test groups for visual training, visual aids, or accessibility evaluations. However, it is crucial to assess the validity and reliability of simulating these types of visual impairments and evaluate the extend to which participants with simulated tunnel vision can represent real patients. Methods: Two age-matched participant groups were acquired: The first group (n=8 aged 20-60, average 49.1, sd 13.2) consisted of patients diagnosed with Retinitis pigmentosa (RP). The second group (n=8, aged 27-59, average 46.5, sd 10.8) consisted of visually healthy participants with simulated tunnel vision. Both groups carried out different visual tasks in a virtual environment for 30 minutes per day over the course of four weeks. Task performances as well as gaze characteristics were evaluated in both groups over the course of the study. Results: Using the "two one-sided tests for equivalence" method, the two groups were found to perform similar in all three visual tasks. Significant differences between groups were found in different aspects of their gaze behavior, though most of these aspects seem to converge over time. Conclusion: Our study evaluates the potential and limitations of using Virtual Reality technology to simulate the effects of tunnel vision within controlled virtual environments. We find that the simulation accurately represents performance of RP patients in the context of group averages, but fails to fully replicate effects on gaze behavior.

We present a simple and accurate GW implementation based on a combination of a Laplace transformation (LT) and other acceleration techniques used in post-SCF quantum chemistry, namely, natural auxiliary functions and the frozen-core approximation. The LT-GW approach combines three major benefits: (a) a small prefactor for the computational scaling, (b) easy integration into existing molecular GW implementations, and (c) significant performance improvements for a wide range of possible applications. Illustrating these advantages for systems consisting of up to 352 atoms and 7412 basis functions, we further demonstrate the benefits of this approach combined with an efficient implementation of the Bethe-Salpeter equation., Comment: added references; added a comparison of various numerical grid-accuracy combinations; added timings for parallel speed-ups in the SI; moved Computational Details to main text; included entire GW100 benchmark set in the main text (incl. statistics)

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multistage machine learning strategy to identify chemically distinct domains in a semi automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real space segmentation via a density based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm(Co,Fe)ZrCu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate like parts by an unsupervised approach based on principle component analysis, or a U-Net based semantic segmentation trained on the former. Following the chemical and geometric analysis, detailed chemical distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped without resorting to the initial voxelization.

In solids, chemical short-range order (CSRO) refers to the self-organisation of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, we showcase how machine learning-enhanced atom probe tomography (APT) can mine the near-atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium-entropy alloy. We reveal multiple CSRO configurations, with their formation supported by state-of-the-art Monte-Carlo simulations. Quantitative analysis of these CSROs allows us to establish relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic-scale architectures.

The study of defect phases is important for designing nanostructured metals and alloys. Grain boundaries (GBs) form one class of defects that directly influence materials properties, such as deformability and strength. At the same time, alloying can introduce GB phase transformations and can therewith alter mechanical performance. In this work, the defect phases of a $\Sigma$7 $(12\bar{3}0)$ [0001] 21.78$^\circ$ symmetric tilt GB in hcp Mg are investigated. Ab-initio simulations as a function of stress and temperature (using quasi-harmonic approximation) are performed, and different types of phase transformations are revealed. To this end, the influence of the chemical degree of freedom on the defect phases is studied for the example of Ga addition, using an efficient screening approach that combines empirical potentials and accurate ab-initio calculations. By exploiting the concept of defect phase diagrams, a phase transformation from the T to the A structural type and as well as a systematic transition of the segregation site preference is revealed. The results qualitatively agree well with experimental observations from scanning transition electron microscopy. The underlying physical mechanisms have an impact on grain-boundary engineering in metallic alloys.

Laves phases often form as secondary phases in metallic alloys and have a significant effect on their structural properties. Thus, phase stability studies for these chemically and structurally complex phases in addition to mechanical behavior studies are of great interest. In this work, we use the concept of metastable bulk phase and defect phase diagrams to augment the understanding of the bulk phase and defect phase stability in Laves phases in Mg-based alloys. In this way, we resolve the discrepancy between bulk phase diagrams and experimental observations regarding the formation of Mg-rich C14 and Al-rich C15 Laves phases in MgAlCa alloys at moderate temperatures. Moreover, the effect of the thermodynamic state of alloys on the competition between solute-rich hcp-like planar defects and stoichiometric basal stacking faults is clarified, which determines the brittleness of these alloys. \end{abstract}

Phase transformations and crystallographic defects are two essential tools to drive innovations in materials. Bulk materials design via tuning chemical compositions has been systematized using phase diagrams. We show here that the same thermodynamic concept can be applied to understand the chemistry at defects. We present a combined experimental and modelling approach to scope and build phase diagrams for defects. The discovery was enabled by triggering phase transformations of individual defects through local alloying, and sequentially imaging the structural and chemical changes using atomic-resolution scanning transmission electron microscopy. By observing atomic-scale phase transformations of a Mg grain boundary through Ga alloying, we exemplified the method to construct a grain boundary phase diagram using ab initio simulations and thermodynamic principles. The methodology enables a systematic development of defect phase diagrams to propel a new paradigm for materials design utilizing chemical complexity and phase transformations at defects.

Thermodynamic bulk phase diagrams have become the roadmap used by researchers to identify alloy compositions and process conditions that result in novel materials with tailored microstructures. Recent experimental studies show that changes in the alloy composition can drive not only transitions in the bulk phases present in a material, but also in the concentration and type of defects they contain. Defect phase diagrams in combination with density functional theory provide a natural route to study these chemically driven defects. Our results show, however, that direct application of thermodynamic approaches can fail to reproduce the experimentally observed defect formation. Therefore, we extend the concept to metastable defect phase diagrams to account for kinetic limitations that prevent the system from reaching equilibrium. We successfully applied this concept to explain the formation of large concentrations of planar defects in supersaturated Fe-Nb solid solutions and to identify in a joint study with experiments conditions in Mg-Al-Ca alloys for defect phase occurrence. The concept offers new avenues for designing materials with tailored defect structures.

La(Fe$_x$Si$_{1-x}$)$_{13}$ and derived quaternary compounds are well-known for their giant, tunable, magneto- and barocaloric responses around a first-order paramagnetic-ferromagnetic transition near room temperature with low hysteresis. Remarkably, such a transition shows a large spontaneous volume change together with itinerant electron metamagnetic features. While magnetovolume effects are well-established mechanisms driving first-order transitions, purely electronic sources have a long, subtle history and remain poorly understood. Here we apply a disordered local moment picture to quantify electronic and magnetoelastic effects at finite temperature in La(Fe$_x$Si$_{1-x}$)$_{13}$ from first-principles. We obtain results in very good agreement with experiment and demonstrate that the magnetoelastic coupling, rather than purely electronic mechanisms, drives the first-order character and causes at the same time a huge electronic entropy contribution to the caloric response., Comment: 20 pages, 6 figures

Low-dimensional materials synthesis based on phosphorus atoms is under intense study, and it is still one of the big challenges. Phosphorene, a monolayer of black phosphorus, is one of the most promising candidates for transistor and photonics devices at atomistic thickness. However, the lack of large-scale and defects-free growth significantly obstructs its device development. Here, we demonstrate the large-scale and defect-free phosphorene synthesis on Nickel (Ni) substrate. In addition, the effect of substrate orientation on the controllable synthesis of possible allotropes has also been described. We have shown that blue phosphorene can be grown on Ni (111) and Ni (100). While {\gamma}-Phosphorene, named Navy Phosphorene hereafter, can be grown on Ni (110). Furthermore, we found that the synthesis goes through phosphorus pentamers (P5) to phosphorene; P5 is a vital precursor for phosphorene synthesis. Moreover, we confirm the high accuracy of the P-Ni, and P-P potentials and show that the molecular dynamics (M.D.) approach is a powerful tool to simulate the 2D materials synthesis in the vapor phase. This work provides a solid reference to understand and control the synthesis of large-area single-crystalline monolayer phosphorene.

We present a computational approach to simulate local contrast observed in Field Ion Microscopy (FIM). It is based on density-functional theory utilizing the Tersoff-Hamann approach as done in Scanning Tunneling Microscopy (STM). A key requirement is the highly accurate computation of the surface states' wave-function tails. To refine the Kohn-Sham states from standard iterative global solvers we introduce and discuss the EXtrapolation of Tails via Reverse integration Algorithm (EXTRA). The decaying tails are obtained by reverse integration (from outside in) using a Numerov-like algorithm. The starting conditions are then iteratively adapted to match the values of plane-wave Kohn-Sham wave functions close to the surface. We demonstrate the performance of the proposed algorithm by analysing and showing the chemical contrast for Ta at Ni surface., Comment: submitted to Phys. Rev. B

This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets. iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+ is conducted at an industrial setting where an autonomous cleaning robot is connected to a private cellular network. The combination of different communication technologies within a common measurement methodology provides insights that can be exploited by Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, prediction of quality of service or link selection. Moreover, the datasets are publicly available, labelled and prefiltered for fast on-boarding and applicability., Comment: 7 pages, 3 figures. Published at IEEE Communications Magazine. IEEE Copyright protected. Datasets available at https://ieee-dataport.org/open-access/ai4mobile-industrial-wireless-datasets-iv2v-and-iv2i

Computational materials science is increasingly benefitting from data management, automation, and algorithm-based decision-making in controlling simulations. Experimental materials science is also undergoing a change and increasingly more `machine learning' is incorporated in materials discovery campaigns. The obvious benefits include automation, reproducibility, data provenance, and reusability of managed data, however, is not widely available. We demonstrate an implementation of a Gaussian Process Regression directly controlling an experimental measurement device in pyiron, a framework designed for high-throughput simulations, as a first step to increasingly combine experimental and simulated data in one framework. With data from both in the same framework, a heretofore untapped and much-needed potential for the acceleration of materials characterization and materials discovery campaigns becomes available., Comment: 17 pages, 4 figures, preprint

Climate change motivates the search for non-carbon-emitting energy generation and storage solutions. Metal hydrides show promising characteristics for this purpose. They can be further stabilized by tailoring the negative pressure of microstructural and structural defects. Using systematic ab initio and atomistic simulations, we demonstrate that an enhancement in the formation of hydrides at the negatively pressurized crack tip region is feasible by increasing the mechanical tensile load on the specimen. The theoretical predictions have been used to reassess and interpret atom probe tomography experiments for a high-strength 7XXX-aluminium alloy that show a substantial enhancement of hydrogen concentration at structural defects near a stress-corrosion crack tip. These results contain important implications for enhancing the capability of metals as H-storage materials., Comment: 22 pages, 9 figures

Hydrogen embrittlement in Zr alloy fuel cladding is a primary safety concern for water based nuclear reactors. Here we investigated the stabilisation of planar defects within the forming hydrides by Sn, the primary alloying element of Zircaloy-4 used in the cladding. In order to explain formation of hydrides and planar defects observed in our experiments, we performed atomic-scale ab initio calculations focusing on the solute interactions with generalized stacking faults in hcp $\alpha$-Zr and fcc zirconium hydrides. Our calculations showed that an increase in Sn concentration leads to a stabilisation of stacking faults in both $\alpha$-Zr and hydride phases. However, the solution enthalpy of Sn is lower in the $\alpha$-Zr as compared to the other hydride phases indicative of two competing processes of Sn depletion/enrichment at the Zr hydride/matrix interface. This is corroborated by experimental findings, where Sn is repelled by hydrides and is mostly found trapped at interfaces and planar defects indicative of stacking faults inside the hydride phases. Our systematic investigation enables us to understand the presence and distribution of solutes in the hydride phases, which provides a deeper insight into the microstructural evolution of such alloy's properties during its service lifetime., Comment: 17 pages, 8 figures

Recording field ion microscope images under field evaporating conditions and subsequently reconstructing the underlying atomic configuration, called three-dimensional field ion microscopy (3D-FIM) is one of the few techniques capable of resolving crystalline defects at an atomic scale. However, the quantification of the observed vacancies and their origins are still a matter of debate. It was suggested that high electric fields (1-5 V/\r{A}) used in 3D-FIM could introduce artefact vacancies. To investigate such effects, we used density functional theory (DFT) simulations. Stepped Ni and Pt surfaces with kinks were modelled in the repeated slab approach with a (971) surface orientation. An electrostatic field of up to 4 V/\r{A} was introduced on one side of the slab using the generalized dipole correction. Contrary to what was proposed, we show that the formation of vacancies on the electrified metal surface is more difficult compared to a field-free case. We also find that the electric field can introduce kinetic barriers to a potential vacancy-annihilation mechanism. We rationalize these findings by comparing to insights from field evaporation models.

Segregation and dissolution behavior of Mg alloyed with Ca and Al are studied by performing density functional theory calculations considering an extensive set of surface structures and compositions. Combining ab initio surface science approaches with cluster expansion for ordered surface structures we construct surface phase diagrams for these alloys. We utilize these diagrams to study segregation phenomena and chemical trends for surfaces in contact with a dry environment or with an aqueous electrolyte. We show that the presence of water dramatically impacts the stability and chemical composition of the considered metallic surfaces. We furthermore find that the two alloying elements behave qualitatively different: whereas Ca strongly segregates to the surface and becomes dissolved upon exposure of the surface to water, Al shows an anti-segregation behavior, i.e., it remains in Mg bulk. These findings provide an explanation for the experimentally observed increase/decrease in corrosion rates when alloying Mg with Al/Ca., Comment: 12 pages, 9 figures, submitted to Phys. Rev. Materials

We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional Active Learning. Using this approach we construct transferable potentials for pure Magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how different types of training structures impact the properties and the predictive power of the resulting potential.

In this paper, we show the existence of a positive solution of a Hammerstein integral equation under certain conditions on the kernel. We apply the recent Layered Compression-Expansion Fixed Point Theorem. Finally, we provide corollaries to help in application of the main results and present an example.

In this paper, we consider a linear fractional differential equation with fractional boundary conditions. First, by obtaining Green's function, we derive the Lyapunov-type inequalities for such boundary value problems. Furthermore, we use the contraction mapping theorem to study the existence of a unique solution to a nonlinear problem.

Fuel cells recombine water from H2 and O2 thereby powering e.g. cars or houses with no direct carbon emission. In anion-exchange membrane fuel cells (AEMFCs), to reach high power densities, operating at high pH is an alternative to using large volumes of noble metals catalysts at the cathode, where the oxygen-reduction reaction occurs. However, the sluggish kinetics of the hydrogen-oxidation reaction (HOR) hinders upscaling despite promising catalysts. Here, we observe an unexpected ingress of B into Pd nano-catalysts synthesised by wet-chemistry, gain control over this B-doping, and report on its influence on the HOR activity in alkaline conditions. We rationalize our findings using ab-initio calculations of both H- and OH-adsorption on B-doped Pd. Using this "impurity engineering" approach, we thus design Pt-free catalysts as required in electrochemical energy conversion devices, e.g. next generations of AEMFCs, that satisfy the economic and environmental constraints, i.e. reasonable operating costs and long-term stability, to enable the hydrogen economy.

High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and feature regimes inaccessible for dilute materials. Discovering those with valuable properties, however, relies on serendipity, as thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. Here, we propose an active-learning strategy to accelerate the design of novel high-entropy Invar alloys in a practically infinite compositional space, based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys (out of millions of possible compositions), we identified 2 high-entropy Invar alloys with extremely low thermal expansion coefficients around 2*10-6 K-1 at 300 K. Our study thus opens a new pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic and electrical properties.

We present a first-principles approach for the computation of the magnetic Gibbs free energy of materials using magnetically constrained supercell calculations. Our approach is based on an adiabatic approximation of slowly varying local moment orientations, the so-called finite-temperature disordered local moment picture. It describes magnetic phase transitions and how electronic and/or magnetostructural mechanisms generate a discontinuous (first-order) character. We demonstrate that the statistical mechanics of the local moment orientations can be described by an affordable number of supercell calculations containing noncollinear magnetic configurations. The applicability of our approach is illustrated by firstly studying the ferromagnetic state in bcc Fe. We then investigate the temperature-dependent properties of a triangular antiferromagnetic state stabilizing in two antiperovskite systems Mn$_3$AN (A = Ga, Ni). Our calculations provide the negative volume expansion of these materials as well as the ab initio origin of the discontinuous character of the phase transitions, electronic and/or magnetostructural, in good agreement with experiment., Comment: 18 pages, 10 figures

We use time-resolved hard x-ray diffraction to investigate the structural dynamics of the multiferroic insulator TbMnO$_3$ in the low temperature antiferromagnetic and ferroelectrically ordered phase. The lattice response following photoexcitation at 1.55 eV is detected by measuring the (0 2 4) and (1 3 -5) Bragg reflections. A 0.02% tensile strain, normal to the surface, is seen to arise within 20 - 30 ps. The magnitude of this transient strain is over an order of magnitude lower than that predicted from laser-induced heating, which we attribute to a bottleneck in the energy transfer between the electronic and lattice subsystems. The timescale for the transient expansion is consistent with that of previously reported demagnetization dynamics. We discuss a possible relationship between structural and demagnetization dynamics in TbMnO$_3$, in which photoinduced atomic motion modulates the exchange interaction, leading to a destruction of the magnetic order in the system., Comment: 3 figures, 4 footnotes

We report on terahertz (THz) electron paramagnetic resonance generalized spectroscopic ellipsometry (THz-EPR-GSE). Measurements of the field and frequency dependencies of the magnetic response due to the spin transitions associated with the nitrogen defect in 4H-SiC are shown as an example. THz-EPR-GSE dispenses with the need of a cavity, permits independently scanning field and frequency parameters, and does not require field or frequency modulation. We investigate spin transitions of hexagonal ($h$) and cubic ($k$) coordinated nitrogen including coupling with its nuclear spin (I=1), and we propose a model approach for the magnetic susceptibility to account for the spin transitions. From the THz-EPR-GSE measurements we can fully determine the polarization properties of the spin transitions and we obtain $g$ and hyperfine splitting parameters using magnetic field and frequency dependent Lorentzian oscillator lineshape functions. We propose frequency-scanning THz-EPR-GSE as a new and versatile method to study properties of spins in solid state materials., Comment: 6 pages, 5 figures

First principles approaches have revolutionized our ability in using computers to predict, explore and design materials. A major advantage commonly associated with these approaches is that they are fully parameter free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations it becomes exceedingly important to achieve a truly parameter free approach. Utilizing uncertainty quantification (UQ) and tensor decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters. Based on this formalism we implement a fully automated approach that requires as input the target accuracy rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice.

Schools are increasingly adopting universal social-emotional learning (SEL) programs to support students' prosocial development and academic success. When adopted across contexts and student populations, SEL interventions can be implemented in different ways particularly under typical classroom conditions that are not part of research efficacy trials. This study, situated across 13 elementary schools, examined 41 primary teachers' use of a popular universal SEL program with their 811 students, with attention to the prevalence and nature of teachers' program changes to standard program practices. In addition, this study explored whether and how teachers' changes were associated with instructional quality more broadly. Results from 221 lesson observations revealed that teachers' instructional expertise in areas closely aligned with the program's target intervention skills was positively associated with higher levels of program fidelity. Expertise was also related to program changes that honored students' outside of school experiences, supported moment-to-moment decision making, and centered on students' interests. [This paper will be published in "Elementary School Journal."]

Social-emotional learning interventions are intended to improve classroom dynamics and have the potential to enhance the well-being of students and their teachers. Using data drawn from an effectiveness trial of the Social Skills Improvement System SEL Edition Classwide Intervention Program (SSIS SEL CIP; Elliott & Gresham, 2017), the present quantitative study explored associations between classroom implementation of a universal SEL program, teachers' emotional well-being, and teacher-student interactions. Results from a sample of 80 first- and second-grade teachers located in three socio-economically and geographically diverse regions of the United States indicated that implementation of the SSIS SEL CIP curriculum was positively associated with teachers' classroom organization skills at the end of the year. Findings also revealed an interaction between treatment condition and teacher emotional well-being such that control teachers with lower well-being also had lower quality classroom organization but this association did not exist for teachers in the intervention condition. Findings suggest that implementation of the SSIS SEL CIP may help to preserve positive teacher-student interactions even when teachers are reporting lower levels of emotional well-being. [This paper was published in "School Mental Health" v15 p190-201 2023 (EJ1370976).]

Cake-cutting is a playful name for the fair division of a heterogeneous, divisible good among agents, a well-studied problem at the intersection of mathematics, economics, and artificial intelligence. The cake-cutting literature is rich and edifying. However, different model assumptions are made in its many papers, in particular regarding the set of allowed pieces of cake that are to be distributed among the agents and regarding the agents' valuation functions by which they measure these pieces. We survey the commonly used definitions in the cake-cutting literature, highlight their strengths and weaknesses, and make some recommendations on what definitions could be most reasonably used when looking through the lens of measure theory.

Ulysses data obtained at high solar latitudes during periods of minimum solar activity in 1994 and 2007 are examined to determine the relation between velocity structures called microstreams and folds in the magnetic field called switchbacks. A high correlation is found. The possibility of velocity peaks in microstreams originating from coronal X-ray jets is re-examined; we now suggest that microstreams are the consequence of the alternation of patches of switchbacks and quiet periods, where the switchbacks could be generated by minifilament/flux rope eruptions that cause coronal jets.

Atom Probe Tomography (APT) analysis is being actively used to provide near-atomic-scale information on the composition of complex materials in three-dimensions. In recent years, there has been a surge of interest in the technique to investigate the distribution of hydrogen in metals. However, the presence of hydrogen in the analysis of almost all specimens from nearly all material systems has caused numerous debates as to its origins and impact on the quantitativeness of the measurement. It is often perceived that most H arises from residual gas ionization, therefore affecting primarily materials with a relatively low evaporation field. In this work, we perform systematic investigations to identify the origin of H residuals in APT experiments by combining density-functional theory (DFT) calculations and APT measurements on an alkali and a noble metal, namely Na and Pt, respectively. We report that no H residual is found in Na metal samples, but in Pt, which has a higher evaporation field, a relatively high signal of H is detected. These results contradict the hypothesis of the H signal being due to direct ionization of residual H$_2$ without much interaction with the specimen's surface. Based on DFT, we demonstrate that alkali metals are thermodynamically less likely to be subject to H contamination under APT-operating conditions compared to transition or noble metals. These insights indicate that the detected H-signal is not only from ionization of residual gaseous H$_2$ alone, but is strongly influenced by material-specific physical properties. The origin of H residuals is elucidated by considering different conditions encountered during APT experiments, specifically, specimen-preparation, transportation, and APT-operating conditions by taking thermodynamic and kinetic aspects into account., Comment: 33 pages, 5 main figures, 5 Supporting Information figures

We present a multi-state implementation of the recently developed FDE-diab methodology [J. Chem. Phys., 148 (2018), 214104] in the Serenity program. The new framework extends the original approach such that any number of charge-localized quasi-diabatic states can be coupled, giving an access to calculations of ground and excited state spin-density distributions as well as to excitation energies. We show that it is possible to obtain results similar to those from correlated wave function approaches such as the complete active space self-consistent field method at much lower computational effort. Additionally, we present a series of approximate computational schemes, which further decrease the overall computational cost and systematically converge to the full FDE-diab solution. The proposed methodology enables computational studies on spin-density distributions and related properties for large molecular systems of biochemical interest.

For many chemical processes the accurate description of solvent effects are vitally important. Here, we describe a hybrid ansatz for the explicit quantum mechanical description of solute-solvent and solvent-solvent interactions based on subsystem density functional theory and continuum solvation schemes. Since explicit solvent molecules may compromise scalability of the model and transferability of the predicted solvent effect, we aim to retain both, for different solutes as well as for different solvents. The key for the transferability is the consistent subsystem decomposition of solute and solvent. The key for the scalability is the performance of subsystem DFT for increasing numbers of subsystems. We investigate molecular dynamics and stationary point sampling of solvent configurations and compare the resulting (Gibbs) free energies to experiment and theoretical methods. We can show that with our hybrid model reaction barriers and reaction energies are accurately reproduced compared to experimental data., Comment: 54 pages, 11 figures

Metal nano-aerogels combine a large surface area, a high structural stability, and a high catalytic activity towards a variety of chemical reactions. The performance of such nanostructures is underpinned by the atomic-level distribution of their constituents. Yet monitoring their sub-nanoscale structure and composition to guide property optimization remains extremely challenging. Here, we synthesized Pd nano-aerogels from a K2PdCl4 precursor and two different NaBH4 reductant concentrations in distilled water. Atom probe tomography reveals that the aerogel is poly-crystalline and that impurities (Na, K) are integrated from the solution into grain boundaries. Ab initio calculations indicate that these impurities preferentially bound to the Pd-metal surface and are ultimately found in grain boundaries forming as the particles coalesce during synthesis, with Na atoms thermodynamically equilibrating with the surrounding solution and K atoms remaining between growing grains. If controlled, impurity integration, i.e. grain boundary decoration, may offer opportunities for designing new nano-aerogels.