arXiv subjects

Marco Hoffmann

Publications and source records attributed to Marco Hoffmann.

15 recordsLinked to original sources

arXiv2025

Real-time dynamics of VCMA-assisted switching of magnetic tunnel junctions

Voltage control of magnetic anisotropy (VCMA) induced by charge accumulation is typically considered as an ultrafast process, enabling energy-efficient and high-speed magnetization switching in spintronic devices. In this work, we investigate the real-time dynamics of VCMA-assisted switching of magnetic tunnel junctions via relaxation in a magnetic field. We show that device-dependent charging effects and magnetic granularity in the free layer limit the switching speed at applied voltages close to the critical switching threshold. Increasing the voltage or the applied magnetic field reduces the incubation delay and total switching time to below a few ns. Micromagnetic simulations incorporating the finite charging times of the tunnel junction and the granularity of the magnetic film reproduce the experimental results, providing critical insights into optimizing VCMA-driven magnetization control for memory and logic applications.

physics.app-ph↗

arXiv2025

Thermodynamically consistent machine learning model for excess Gibbs energy

The excess Gibbs energy plays a central role in chemical engineering and chemistry, providing a basis for modeling thermodynamic properties of liquid mixtures. Predicting the excess Gibbs energy of multi-component mixtures solely from molecular structures is a long-standing challenge. We address this challenge with HANNA, a flexible machine learning model for excess Gibbs energy that integrates physical laws as hard constraints, guaranteeing thermodynamically consistent predictions. HANNA is trained on experimental data for vapor-liquid equilibria, liquid-liquid equilibria, activity coefficients at infinite dilution and excess enthalpies in binary mixtures. The end-to-end training on liquid-liquid equilibrium data is facilitated by a surrogate solver. A geometric projection method enables robust extrapolations to multi-component mixtures. We demonstrate that HANNA delivers accurate predictions, while providing a substantially broader domain of applicability than state-of-the-art benchmark methods. The trained model and corresponding code are openly available, and an interactive interface is provided on our website, MLPROP.

cs.LG↗

arXiv2025

Superstudent intelligence in thermodynamics

In this short note, we report and analyze a striking event: OpenAI's large language model o3 has outwitted all students in a university exam on thermodynamics. The thermodynamics exam is a difficult hurdle for most students, where they must show that they have mastered the fundamentals of this important topic. Consequently, the failure rates are very high, A-grades are rare - and they are considered proof of the students' exceptional intellectual abilities. This is because pattern learning does not help in the exam. The problems can only be solved by knowledgeably and creatively combining principles of thermodynamics. We have given our latest thermodynamics exam not only to the students but also to OpenAI's most powerful reasoning model, o3, and have assessed the answers of o3 exactly the same way as those of the students. In zero-shot mode, the model o3 solved all problems correctly, better than all students who took the exam; its overall score was in the range of the best scores we have seen in more than 10,000 similar exams since 1985. This is a turning point: machines now excel in complex tasks, usually taken as proof of human intellectual capabilities. We discuss the consequences this has for the work of engineers and the education of future engineers.

cs.CE↗

arXiv2025

MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning

Machine learning (ML) enables the development of powerful methods for predicting thermophysical properties with unprecedented scope and accuracy. However, technical barriers like cumbersome implementation in established workflows hinder their application in practice. With MLPROP, we provide an interactive web interface for directly applying advanced ML methods to predict thermophysical properties without requiring ML expertise, thereby substantially increasing the accessibility of novel models. MLPROP currently includes models for predicting the vapor pressure of pure components (GRAPPA), activity coefficients and vapor-liquid equilibria in binary mixtures (UNIFAC 2.0, mod. UNIFAC 2.0, and HANNA), and a routine to fit NRTL parameters to the model predictions. MLPROP will be continuously updated and extended and is accessible free of charge via https://ml-prop.mv.rptu.de/. MLPROP removes the barrier to learning and experimenting with new ML-based methods for predicting thermophysical properties. The source code of all models is available as open source, which allows integration into existing workflows.

cs.CE↗

arXiv2025

Using Large Language Models for Solving Thermodynamic Problems

Large Language Models (LLMs) have made significant progress in reasoning, demonstrating their capability to generate human-like responses. This study analyzes the problem-solving capabilities of LLMs in the domain of thermodynamics. A benchmark of 22 thermodynamic problems to evaluate LLMs is presented that contains both simple and advanced problems. Five different LLMs are assessed: GPT-3.5, GPT-4, and GPT-4o from OpenAI, Llama 3.1 from Meta, and le Chat from MistralAI. The answers of these LLMs were evaluated by trained human experts, following a methodology akin to the grading of academic exam responses. The scores and the consistency of the answers are discussed, together with the analytical skills of the LLMs. Both strengths and weaknesses of the LLMs become evident. They generally yield good results for the simple problems, but also limitations become clear: The LLMs do not provide consistent results, they often fail to fully comprehend the context and make wrong assumptions. Given the complexity and domain-specific nature of the problems, the statistical language modeling approach of the LLMs struggles with the accurate interpretation and the required reasoning. The present results highlight the need for more systematic integration of thermodynamic knowledge with LLMs, for example, by using knowledge-based methods.

cs.CE↗

arXiv2025

GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures

Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we have developed GRAPPA - a hybrid graph neural network for predicting vapor pressures of pure components. GRAPPA enables the prediction of the vapor pressure curve of basically any organic molecule, requiring only the molecular structure as input. The new model consists of three parts: A graph attention network for the message passing step, a pooling function that captures long-range interactions, and a prediction head that yields the component-specific parameters of the Antoine equation, from which the vapor pressure can readily and consistently be calculated for any temperature. We have trained and evaluated GRAPPA on experimental vapor pressure data of almost 25,000 pure components. We found excellent prediction accuracy for unseen components, outperforming state-of-the-art group contribution methods and other machine learning approaches in applicability and accuracy. The trained model and its code are fully disclosed, and GRAPPA is directly applicable via the interactive website ml-prop.mv.rptu.de.

cs.LG↗

arXiv2024

Pulse Shaping Strategies for Efficient Switching of Magnetic Tunnel Junctions by Spin-Orbit Torque

The writing energy for reversing the magnetization of the free layer in a magnetic tunnel junction (MTJ) is a key figure of merit for comparing the performances of magnetic random access memories with competing technologies. Magnetization switching of MTJs induced by spin torques typically relies on square voltage pulses. Here, we focus on the switching of perpendicular MTJs driven by spin-orbit torque (SOT), for which the magnetization reversal process consists of sequential domain nucleation and domain wall propagation. By performing a systematic study of the switching efficiency and speed as a function of pulse shape, we show that shaped pulses achieve up to 50% reduction of writing energy compared to square pulses without compromising the switching probability and speed. Time-resolved measurements of the tunneling magnetoresistance reveal how the switching times are strongly impacted by the pulse shape and temperature rise during the pulse. The optimal pulse shape consists of a preheating phase, a maximum amplitude to induce domain nucleation, and a lower amplitude phase to complete the reversal. Our experimental results, corroborated by micromagnetic simulations, provide diverse options to reduce the energy footprint of SOT devices in magnetic memory applications.

physics.app-ph↗

arXiv2022

Tailoring the switching efficiency of magnetic tunnel junctions by the fieldlike spin-orbit torque

Current-induced spin-orbit torques provide a versatile tool for switching magnetic devices. In perpendicular magnets, the dampinglike component of the torque is the main driver of magnetization reversal. The degree to which the fieldlike torque assists the switching is a matter of debate. Here we study the switching of magnetic tunnel junctions with a CoFeB free layer and either W or Ta underlayers, which have a ratio of fieldlike to dampinglike torque of 0.3 and 1, respectively. We show that the fieldlike torque can either assist or hinder the switching of CoFeB when the static in-plane magnetic field required to define the polarity of spin-orbit torque switching has a component transverse to the current. In particular, the non-collinear alignment of the field and current can be exploited to increase the switching efficiency and reliability compared to the standard collinear alignment. By probing individual switching events in real-time, we also show that the combination of transverse magnetic field and fieldlike torque can accelerate or decelerate the reversal onset. We validate our observations using micromagnetic simulations and extrapolate the results to materials with different torque ratios. Finally, we propose device geometries that leverage the fieldlike torque for density increase in memory applications and synaptic weight generation.

cond-mat.mes-hall↗

arXiv2021

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from safety concerns. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. This work provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern ML methods. We moreover hope that our contribution fuels discussions on desiderata for ML systems and strategies on how to propel existing approaches accordingly.

cs.LG↗

arXiv2021

The human side of Software Engineering Teams: an investigation of contemporary challenges

There have been recent calls for research on the human side of software engineering and its impact on various factors such as productivity, developer happiness and project success. An analysis of which challenges in software engineering teams are most frequent is still missing. We aim to provide a starting point for a theory about relevant human challenges and their causes in software engineering. We establish a reusable set of challenges and start out by investigating the effect of team virtualization. Virtual teams often use digital communication and consist of members with different nationalities. We designed a survey instrument and asked respondents to assess the frequency and criticality of a set of challenges, separated in context "within teams" as well as "between teams and clients", compiled from previous empiric work, blog posts and pilot survey feedback. For the team challenges, we asked if mitigation measures were already in place. Respondents were also asked to provide information about their team setup. The survey also measured Schwartz human values. Finally, respondents were asked if there were additional challenges at their workplace. We report on the results obtained from 192 respondents. We present a set of challenges that takes the survey feedback into account and introduce two categories of challenges; "interpersonal" and "intrapersonal". We found no evidence for links between human values and challenges. We found some significant links between the number of distinct nationalities in a team and certain challenges, with less frequent and critical challenges occurring if 2-3 different nationalities were present compared to a team having members of just one nationality or more than three. A higher degree of virtualization seems to increase the frequency of some human challenges.

cs.SE↗

arXiv2020

Structural and physical properties of trilayer nickelates $R_4$Ni$_3$O$_{10}$ ($R =$ La, Pr and Nd)

We investigate the low temperature structural and physical properties of the trilayer nickelates R4Ni3O10 (R = La, Pr and Nd) using resistivity, thermopower, thermal conductivity, specific heat, high-resolution synchrotron powder X-ray diffraction and thermal expansion experiments. We show that all three compounds crystallize with a monoclinic symmetry, and undergo a metal-to-metal (MMT) transition at 135 K (La), 156 K (Pr) and 160 K (Nd). At MMT, the lattice parameters show distinct anomalies; however, without any lowering of the lattice symmetry. Unambiguous signatures of MMT are also seen in magnetic and thermal measurements, which suggest a strong coupling between the electronic, magnetic and structural degrees of freedom in these nickelates. Analysis of thermal expansion yields hydrostatic pressure dependence of MMT in close agreement with experiments. We show that the 9-fold coordinated Pr ions in the rocksalt (RS) layers have a crystal field (CF) split doublet ground state with possible antiferromagnetic ordering at 5 K. The Pr ions located in the perovskite block (PB) layers with 12-fold coordination, however, exhibit a non-magnetic singlet ground state. The CF ground state of Nd in both RS and PB layers is a Kramers doublet. Heat capacity of R = Nd shows a Schottky-like anomaly near35 K, and an upturn below T = 10 K suggesting the presence of short-range correlations between the Nd moments. However, no signs of long-range ordering could be found down to 2 K despite a sizeable theta_p ~ -40 K. The strongly suppressed magnetic long-range ordering in both R = Pr and Nd suggests the presence of strong magnetic frustration in these compounds. The low-temperature resistivity shows a T^0.5 dependence. No evidence for the heavy fermion behavior could be found in any of the three compounds.

cond-mat.str-el↗

arXiv2016

SDN Partitioning: A Centralized Control Plane for Distributed Routing Protocols

Hybrid IP networks that use both control paradigms - distributed and centralized - promise the best of two worlds: programmability and agility of SDN, and reliability and fault tolerance of distributed routing protocols like OSPF. The common approaches follow a division of labor concept, where SDN controls prioritized traffic and OSPF assures care-free operation of best effort traffic. We propose SDN Partitioning, which establishes centralized control over the distributed routing protocol by partitioning the topology into sub-domains with SDN-enabled border nodes, such that OSPF's routing updates have to traverse SDN border nodes to reach neighboring sub-domains. This allows the central controller to modify how sub-domains view one another, which in turn allows to steer inter-sub-domain traffic. The degree of dynamic control against simplicity of OSPF can be trade off by adjusting the size of the sub-domains. This paper explains the technical requirements, presents a novel scheme for balanced topology partitioning, and provides the models for common network management tasks. Our performance evaluation shows that - already in its minimum configuration with two sub-domains - SDN Partitioning provides significant improvements in all respects compared to legacy routing protocols, whereas smaller sub-domains provide network control capabilities comparable to full SDN deployment.

cs.NI↗

arXiv2014

Divide and Conquer: Partitioning OSPF networks with SDN

Software Defined Networking (SDN) is an emerging network control paradigm focused on logical centralization and programmability. At the same time, distributed routing protocols, most notably OSPF and IS-IS, are still prevalent in IP networks, as they provide shortest path routing, fast topological convergence after network failures, and, perhaps most importantly, the confidence based on decades of reliable operation. Therefore, a hybrid SDN/OSPF operation remains a desirable proposition. In this paper, we propose a new method of hybrid SDN/OSPF operation. Our method is different from other hybrid approaches, as it uses SDN nodes to partition an OSPF domain into sub-domains thereby achieving the traffic engineering capabilities comparable to full SDN operation. We place SDN-enabled routers as sub-domain border nodes, while the operation of the OSPF protocol continues unaffected. In this way, the SDN controller can tune routing protocol updates for traffic engineering purposes before they are flooded into sub-domains. While local routing inside sub-domains remains stable at all times, inter-sub-domain routes can be optimized by determining the routes in each traversed sub-domain. As the majority of traffic in non-trivial topologies has to traverse multiple sub-domains, our simulation results confirm that a few SDN nodes allow traffic engineering up to a degree that renders full SDN deployment unnecessary.

cs.NI↗

arXiv2014

Study of Network Migration to New Technologies using Agent-based Modeling Techniques

Conventionally, network migration models study competition between emerging and incumbent technologies by considering the resulting increase in revenue and associated cost of migration. We propose to advance the science in the existing network migration models by considering additional critical factors, including (i) synergistic relationships across multiple technologies, (ii) reduction in operational expenditures (OpEx) as a reason to migrate, and, (iii) implications of local network effects on migration decisions. To this end, we propose a novel agent-based migration model considering these factors. Based on the model, we analyze the case study of network migration to two emerging networking paradigms, i.e., IETF Path Computation Element (PCE) and Software-Defined Networking (SDN). We validate our model using extensive simulations. Our results demonstrate the synergistic effects of migration to multiple complementary technologies, and show that a technology migration may be eased by the joint migration to multiple technologies. In particular, we find that migration to SDN can be eased by joint migration to PCE, and that the benefits derived from SDN are best exploited in combination with PCE, than by itself.

cs.NI↗

arXiv2013

A Techno-economic Analysis of Network Migration to Software-Defined Networking

As the Software Defined Networking (SDN) paradigm gains momentum, every network operator faces the obvious dilemma: when and how to migrate from existing IP routers to SDN compliant equipments. A single step complete overhaul of a fully functional network is impractical, while at the same time, the immediate benefits of SDN are obvious. A viable solution is thus a gradual migration over time, where questions of which routers should migrate first, and whether the order of migration makes a difference, can be analyzed from techno economic and traffic engineering perspective. In this paper, we address these questions from the techno economic perspective, and establish the importance of migration scheduling. We propose optimization techniques and greedy algorithms to plan an effective migration schedule, based on various techno economic aspects, such as technological gains in combinations with CapEx limitations. We demonstrate the importance of an effective migration sequence through two relevant network management metrics, namely, number of alternative paths availed by a node on migration, and network capacity savings. Our results suggest that the sequence of migration plays a vital role, especially in the early stages of network migration to SDN.

cs.NI↗