arXiv subjects

Florian Mayer

Publications and source records attributed to Florian Mayer.

11 recordsLinked to original sources

arXiv2026

Pulse-Driven Reconfiguration of Fractional Polar Topology in Zr-Substituted Barium Titanate

Polar topological textures in ferroelectrics can host internal structure beyond a single integer topological charge. Here, effective-Hamiltonian molecular-dynamics simulations are used to examine whether such internal fractional topology can be reconfigured by local electric excitation in ordered 12.5% Zr-substituted barium titanate. Chemical doubling along the polar axis stabilizes a coupled nanodomain texture consisting of alternating Q = -2 antiskyrmionic and Q = +4 skyrmionic slices, in which the local topological charge fragments into six -1/3 and six +2/3 localized contributions, denoted here as topological quarks, separated by Bloch-point-like singular conversion regions. Picosecond local electric-field pulses applied to selected vortex-core columns drive reconfiguration of the internal dipolar texture of a 2.6 nm nanodomain. Under a binary pulse-mask protocol addressing the six vortex cores, all 64 masks lead, within the chosen low-temperature simulation protocol, to distinct relaxed metastable configurations. The switching calculations are performed in a cryogenic regime, and the programmed states remain stable over at least 1 ns of field-free evolution on the simulation timescale. The resulting configurations are distinguishable both by sector-resolved topological fingerprints and by their real-space polarization fields. These results provide a computational proof of concept that fractional polar topology in a ferroelectric nanodomain can be locally reconfigured by ultrafast electric excitation and used as a multistate configurational degree of freedom in an idealized low-temperature setting.

cond-mat.mtrl-sci↗

arXiv2026

Loop-level surrogate modeling of dopant-distribution effects in Ba(Zr,Ti)O$_3$

Barium titanate-based perovskites are important candidates for lead-free dielectric and electromechanical technologies. In Zr-substituted BaTiO$_3$ (BZT), functional behavior is usually discussed in terms of the average Zr concentration, while the influence of dopant spatial distribution beyond average concentration is less understood and difficult to explore systematically. Here we present an accelerated materials-design workflow that links controlled dopant distributions to full field-driven response curves. We generate a broad set of Zr distributions spanning a continuum of nanoscale arrangements, with layers, rods, dots, and lamellae serving as representative end-member motifs, and encode each configuration using a compact, parametrized descriptor model. Effective-Hamiltonian molecular dynamics is used to compute polarization-electric-field and strain-field hysteresis loops, and we train a conditional autoencoder surrogate to predict complete loops directly from the distribution parameters. This surrogate enables rapid screening and dense, property-selective design maps at scales that are not feasible with direct simulations alone, and it supports targeted follow-up simulations in regions of interest. Using the predicted loop database, we screen the distribution space for multiple functional targets, including energy-storage performance, electromechanical response, and switching behavior, and identify the corresponding dopant distribution motif families. The resulting design maps show that dopant distribution is an independent tuning parameter that can strongly affect hysteresis behavior and loop-derived figures of merit: layer-like motifs, vertical lamellae, and nanoplate-like inclusions emerge in different performance regimes. More generally, predicting full response curves enables screening of other loop-derived targets and multi-objective design in substituted ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2025

Event-Chain Analysis for Automated Driving and ADAS Systems: Ensuring Safety and Meeting Regulatory Timing Requirements

Automated Driving Systems (ADS), including Advanced Driver Assistance Systems (ADAS), must fulfill not only high functional expectations but also stringent timing constraints mandated by international regulations and standards. Regulatory frameworks such as UN regulations, NCAP standards, ISO norms, and NHTSA guidelines impose strict bounds on system reaction times to ensure safe vehicle operation. This paper presents a structured, White-Box methodology based on Event-Chain Modeling to address these timing challenges. Unlike Black-Box approaches, Event-Chain Analysis offers transparent insights into the timing behavior of each functional component - from perception and planning to actuation and human interaction. This perspective is also aligned with multiple regulations, which require that homologation dossiers provide evidence that the chosen system architecture is suitable to ensure compliance with the specified requirements. Our methodology enables the derivation, modeling, and validation of end-to-end timing constraints at the architectural level and facilitates early verification through simulation. Through a detailed case study, we demonstrate how this Event-Chain-centric approach enhances regulatory compliance, optimizes system design, and supports model-based safety analysis techniques, with results showing early identification of compliance issues, systematic parameter optimization, and quantitative evidence generation through probabilistic analysis.

cs.SE↗

arXiv2025

Topological Textures in Zr-Substituted Barium Titanate

Topological polarization textures in ferroelectrics offer pathways to dense memory, neuromorphic computing, and controlled probes of topology in solids. In rhombohedral barium titanate, theory has identified stable antiskyrmions of topological charge -2 that fractionalize into six -1/3 hotspots, termed topological quarks. Here we extend this landscape to Zr-substituted barium titanate (BZT) using a first-principles parameterized effective Hamiltonian framework. In an ordered 12.5% composition, the chemically doubled periodicity enforces an alternation along [111]: one half hosts the -2 antiskyrmion (six -1/3 quarks), the other a +4 skyrmion (six +2/3 quarks). The two share the same six-vortex scaffold (threefold motif) but differ in the core-level polarization texture, resulting in an integer +1 per vortex offset in the plane-integrated (slice) topological charge. In random BZT, nanodomains remain inducible and cryogenically stable, yet quenched disorder pins and distorts the vortices, producing a heterogeneous, skyrmion-glass-like state with fluctuations of the topological charge along the nanodomain axis. Thermal stability maps show that pure barium titanate retains -2 textures up to ~100 K, whereas in BZT the collapse temperature is nonmonotonic, with a minimum near 6-8% Zr, reflecting competition between ferroelectric softening and disorder pinning. Importantly, the 12.5% ordered arrangement remains rhombohedral above 300 K, enabling field-stabilized nanodomains at 293 K. Under a local [111] bias, the ordered system carries +4 slice charge, while the random composition fragments under the same conditions. These results establish BZT as a platform for chemically programmed, fractionalized ferroelectric topology from cryogenic to room temperature and suggest routes to multistate, reconfigurable devices.

cond-mat.mtrl-sci↗

arXiv2025

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned

There is an increasing adoption of artificial intelligence in safety-critical applications, yet practical schemes for certifying that AI systems are safe, lawful and socially acceptable remain scarce. This white paper presents the T\"UV AUSTRIA Trusted AI framework an end-to-end audit catalog and methodology for assessing and certifying machine learning systems. The audit catalog has been in continuous development since 2019 in an ongoing collaboration with scientific partners. Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - the catalog translates the high-level obligations of the EU AI Act into specific, testable criteria. Its core concept of functional trustworthiness couples a statistically defined application domain with risk-based minimum performance requirements and statistical testing on independently sampled data, providing transparent and reproducible evidence of model quality in real-world settings. We provide an overview of the functional requirements that we assess, which are oriented on the lifecycle of an AI system. In addition, we share some lessons learned from the practical application of the audit catalog, highlighting common pitfalls we encountered, such as data leakage scenarios, inadequate domain definitions, neglect of biases, or a lack of distribution drift controls. We further discuss key aspects of certifying AI systems, such as robustness, algorithmic fairness, or post-certification requirements, outlining both our current conclusions and a roadmap for future research. In general, by aligning technical best practices with emerging European standards, the approach offers regulators, providers, and users a practical roadmap for legally compliant, functionally trustworthy, and certifiable AI systems.

cs.CY↗

arXiv2024

Thermal Stability and Topological Charge Fragmentation in Antiskyrmions of Rhombohedral Barium Titanate

Antiskyrmions, as topological quasi-particles, hold significant promise for spintronics and nanoscale data storage applications. Using molecular dynamics simulations based on effective Hamiltonians, we investigate the thermal stability of antiskyrmion nanodomains in rhombohedral barium titanate. At 1 K, antiskyrmions with a topological charge of -2 emerge as the most stable nanodomain state across all examined diameters. In our systematic study, the most robust antiskyrmion was found to have a diameter of 4 nm, maintaining its original size, shape, and topological charge up to the characteristic temperature T* ~ 85 K. Domains with diameters between 2.8 and approximately 4.5 nm exhibit fragmentation into six topological defects, termed quarks, each carrying a fractional skyrmion charge of -1/3. For domains larger than 4.5 nm, each topological quark splits into two pre-quarks, each with a charge of -1/6. These larger nanodomains demonstrate increased mobility and a growing tendency for shape and skyrmion charge fluctuations. Above the T* temperature, all larger nanodomains gradually shrink to a diameter of about 4 nm before collapsing into a single-domain state. These findings reveal the relatively high stability of antiskyrmions over a broad temperature range, even in the absence of a stabilizing bias field, and emphasize the pivotal role of topological quark dynamics. This establishes barium titanate as a key platform for exploring and applying topological phenomena.

cond-mat.mtrl-sci↗

arXiv2024

Beyond Beats: A Recipe to Song Popularity? A machine learning approach

Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics and their impact on popularity. Results: Ordinary Least Squares (OLS) regression analysis reveals genre as the primary influencer of popularity, with notable trends over time. MARS modelling highlights the complex relationship between variables, particularly with features like instrumentalness and duration. Random Forest and XGBoost models underscore the importance of genre, especially EDM, in predicting popularity. Despite variations in performance, Random Forest emerges as the most effective model, improving prediction accuracy by 7.1% compared to average scores. Despite the importance of genre, predicting song popularity remains challenging, as observed variations in music-related features suggest complex interactions between genre and other factors. Consequently, while certain characteristics like loudness and song duration may impact popularity scores, accurately predicting song success remains elusive.

cs.IR↗

arXiv2024

Employing polyhedral methods to optimize stencils on FPGAs with stencil-specific caches, data reuse, and wide data bursts

It is well known that to accelerate stencil codes on CPUs or GPUs and to exploit hardware caches and their lines optimizers must find spatial and temporal locality of array accesses to harvest data-reuse opportunities. On FPGAs there is the burden that there are no built-in caches (or only pre-built hardware descriptions for cache blocks that are inefficient for stencil codes). But this paper demonstrates that this lack is also a chance as polyhedral methods can be used to generate stencil-specific cache-structures of the right sizes on the FPGA and to fill and flush them efficiently with wide bursts during stencil execution. The paper shows how to derive the appropriate directives and code restructurings from stencil codes so that the FPGA compiler generates fast stencil hardware. Switching on our optimization improves the runtime of a set of 10 stencils by between 43x and 156x.

cs.PL↗

arXiv2021

Origin of relaxor behavior in barium-titanate based lead-free perovskites

It is well known that disordered relaxor ferroelectrics exhibit local polar correlations. The origin of localized fields that disrupt long range polar order for different substitution types, however, is unclear. Currently, it is known that substituents of the same valence as Ti4+ at the B-site of barium titanate lattice produce random disruption of Ti-O-Ti chains that induces relaxor behavior. On the other hand, investigating lattice disruption and relaxor behavior resulting from substituents of different valence at the B-site is more complex due to the simultaneous occurrence of charge imbalances and displacements of the substituent cation. The existence of an effective charge mediated mechanism for relaxor behavior appearing at low (< 10%) substituent contents in heterovalent modified barium titanate ceramics is presented in this work. These results will add credits to the current understanding of relaxor behavior in chemically modified ferroelectric materials and also acknowledge the critical role of defects (such as cation vacancies) in lattice disruption, paving the way for chemistry-based materials design in the field of dielectric and energy storage applications.

cond-mat.mtrl-sci↗

arXiv2019

Understanding the correlation between electronic coupling and energetic stability of molecular crystal polymorphs: The instructive case of quinacridone

A crucial factor determining charge transport in organic semiconductors is the electronic coupling between the molecular constituents, which is heavily influenced by the relative arrangement of the molecules. This renders quinacridone, with its multiple, structurally fundamentally different polymorphs and their diverse intermolecular interactions an ideal test case for analyzing the correlation between the electronic coupling in a specific configuration and the configuration's energetic stability. To provide an in-depth analysis of this correlation, starting from the $α$-polymorph of quinacridone, we also construct a coplanar model crystal. This allows us to systematically compare the displacement-dependence of the electronic coupling with that of the total energy. In this way, we identify the combination of Pauli repulsion and orbital rehybridization as the driving force steering the system towards a structure in which the electronic coupling is minimal (especially for the valence band and at small displacements). The general nature of these observations is supported by equivalent trends for an analogous pentacene model system. This underlines that the design of high-performance materials cannot rely on the "natural" assembly of the $π$-conjugated backbones of organic semiconductors into their most stable configurations. Rather, it must include the incorporation of functional groups that steer crystal packing towards more favorable structures, where aiming for short-axis displacements or realizing comparably large long-axis displacements appear as strategies worthwhile exploring.

cond-mat.mtrl-sci↗

arXiv2015

SunPy - Python for Solar Physics

This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualisation and plotting (matplotlib). SunPy is a data-analysis environment specialising in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy.

astro-ph.IM↗