arXiv subjects

Christoph Müller

Publications and source records attributed to Christoph Müller.

At least 19 recordsLinked to original sources

arXiv2026

Fermi-surface studies of altermagnetic CrSb from Shubnikov-de Haas oscillations

Within the family of altermagnets, CrSb is a metallic, collinearly ordered material that exhibits particularly strong symmetry-induced spin splitting in its band structure. In this study, we combine electrical magnetotransport measurements up to 68 T on microfabricated single-crystalline CrSb with first-principles calculations to investigate its Fermi surface. Notably, we study the temperature and field-orientation dependence of magnetic quantum oscillations observed in the magnetoresistance. The observed frequency spectrum agrees well with results from density-functional-theory calculations. Our results confirm the predicted electronic band structure of altermagnetic CrSb and highlight the importance of high magnetic fields for accurately mapping the Fermi surfaces of unconventional emergent materials.

cond-mat.mtrl-sci↗

arXiv2026

Integrating a Python Dynamical core into ICON

The transition of Earth-system models to exascale is often hindered by rigid, monolithic Fortran codebases and maintenance-heavy compiler directives. While high-level DSLs offer a solution, they frequently fail due to cumbersome integration. We present the integration of a Python-based ICON dynamical core into the original Fortran simulation code. Leveraging the GT4Py DSL and the Data-Centric (DaCe) optimization framework, we demonstrate that high-level Python can be seamlessly integrated into legacy infrastructure without performance loss. Our results challenge the assumption that Python orchestration introduces prohibitive HPC overhead. In production-grade global simulations, our Python dynamical core achieves a 20--30\% performance improvement over the highly-optimized Fortran+OpenACC implementation, with a 10\% improvement on the total time for a coupled setup. Driven by advanced data-flow optimizations and automated kernel fusion, this approach replaces hardware-entangled directives by generating optimized device code from a single, portable Python source. This work proves that Python can provide a sustainable, efficient, and hardware-agnostic future for global climate modeling.

cs.DC↗

arXiv2026

NeuralCrop: Combining physics and machine learning for improved crop yield projections

Global gridded crop models (GGCMs) are crucial to project the impacts of climate change on agricultural productivity and assess associated risks for food security. Despite decades of development, state-of-the-art GGCMs retain substantial uncertainties stemming from process representations. Recently, machine learning approaches trained on observational data provide alternatives in crop yield projections. However, these models have not demonstrated improved performance over traditional GGCMs and are not suitable for projecting crop yields under a changing climate due to their poor out-of-distribution generalization. Here we introduce NeuralCrop, a differentiable hybrid GGCM that combines the strengths of an advanced process-based GGCM, resolving important processes explicitly, with data-driven machine learning components. NeuralCrop is first trained to emulate a competitive GGCM before it is fine-tuned on observational data. We show that NeuralCrop produces projections with accuracy comparable to state-of-the-art GGCMs across site-level and large-scale crop simulations. NeuralCrop can accurately project the interannual yield variability in European wheat regions and the US Corn Belt. Capturing yield anomalies is essential for developing adaptation strategies in the context of climate change. NeuralCrop can more accurately reproduce yield anomalies across various climatic conditions, with particularly notable improvements under drought extremes. For large-scale, long-term simulations, our approach is orders of magnitude more computationally efficient. Our results show that end-to-end hybrid crop modelling offers more reliable yield projections that are essential for food risk assessments under climate change and intensifying extreme weather events.

cs.LG↗

arXiv2026

Electrical and thermal magnetotransport in altermagnetic CrSb

Chromium antimonide has emerged as a key material platform for studying altermagnetism because of its simple binary composition, high Néel temperature, and semimetallic electronic structure. Here, we investigate electrical and thermal magnetotransport in single-crystalline CrSb using steady-and pulsed-magnetic fields up to 65 T, and complement these measurements with neutron diffraction and magnetization data. We confirm the compensated magnetic structure and observe a large nonsaturating magnetoresistance together with a pronounced nonlinear Hall response at low temperatures. Multicarrier modeling, supported by mobility-spectrum analysis, reveals coexisting electron- and hole-like charge carriers with mobilities up to ~3000 cm2/Vs and shows that the number of transport channels that can be resolved strongly depends on the accessible magnetic-field range. Thermal-transport measurements further reveal a nonlinear thermal Hall response and a thermal conductivity substantially exceeding a simple Wiedemann-Franz law. The broadly similar field and temperature evolution of electrical and thermal transport point to a dominant electronic contribution, while the remaining deviations indicate additional heat-carrying channels.

cond-mat.mtrl-sci↗

arXiv2025

HEEPidermis: a versatile SoC for BioZ recording

Biological impedance (BioZ) is an information-packed modality that allows for non-invasive monitoring of health and emotional state. Currently, most research involving tissue impedance is based on bulky or fixed-purpose hardware, which limits the scope of research and the possibilities of experiments. In this work, we present HEEPidermis: a System-on-Chip (SoC) which integrates all the blocks needed for tissue impedance measurement, including two 8-bit, arbitrary-signal current DACs, two VCO-based ADCs, and a RISC-V CPU to enable on-chip feature extraction for closed-loop operation. An event-based sub-sampler improves storage and energy efficiency for long-term recording. In addition to the versatile SoC, the digital back-end and behavioral models of the analog front-end are open-source, allowing fast system-level simulations or repurposing. The SoC was taped out on TSMC 65 nm LP process.

physics.ins-det↗

arXiv2025

Spin Hall magnetoresistance at the altermagnetic insulator/Pt interface

The resistance of a heavy metal can be modulated by an adjacent magnetic material through the combined effects of the spin Hall effect, inverse spin Hall effect, and dissipation of the spin accumulation at the interface. This phenomenon is known as the spin Hall magnetoresistance. The dissipation of the spin accumulation can occur via various mechanisms, with spin-transfer torque being the most extensively studied. In this work, we report the observation of spin Hall magnetoresistance at the interface between platinum and an insulating altermagnetic candidate, Ba$_2$CoGe$_2$O$_7$. Our findings reveal that this heterostructure exhibits a relatively large spin Hall magnetoresistance signal, which is anisotropic with respect to the crystal orientation of the current channel. We explore and rule out several potential explanations for this anisotropy and propose that our results may be understood in the context of the anisotropies of the spin current channels across the Pt/altermagnetic Ba$_2$CoGe$_2$O$_7$ interface.

cond-mat.mes-hall↗

arXiv2024

The effect of niobium thin film structure on losses in superconducting circuits

The performance of superconducting microwave circuits is strongly influenced by the material properties of the superconducting film and substrate. While progress has been made in understanding the importance of surface preparation and the effect of surface oxides, the complex effect of superconductor film structure on microwave losses is not yet fully understood. In this study, we investigate the microwave properties of niobium resonators with different crystalline properties and related surface topographies. We analyze a series of magnetron sputtered films in which the Nb crystal orientation and surface topography are changed by varying the substrate temperatures between room temperature and 975 K. The lowest-loss resonators that we measure have quality factors of over one million at single-photon powers, among the best ever recorded using the Nb on sapphire platform. We observe the highest quality factors in films grown at an intermediate temperature regime of the growth series (550 K) where the films display both preferential ordering of the crystal domains and low surface roughness. Furthermore, we analyze the temperature-dependent behavior of our resonators to learn about how the quasiparticle density in the Nb film is affected by the niobium crystal structure and the presence of grain boundaries. Our results stress the connection between the crystal structure of superconducting films and the loss mechanisms suffered by the resonators and demonstrate that even a moderate change in temperature during thin film deposition can significantly affect the resulting quality factors.

quant-ph↗

arXiv2023

Towards a unified picture of polarization transfer -- pulsed DNP and chemically equivalent PHIP

Nuclear spin hyperpolarization techniques, such as dynamic nuclear polarization (DNP) and parahydrogen-induced polarization (PHIP), have revolutionized nuclear magnetic resonance and magnetic resonance imaging. In these methods, a readily available source of high spin order, either electron spins in DNP or singlet states in hydrogen for PHIP, is brought into close proximity with nuclear spin targets, enabling efficient transfer of spin order under external quantum control. Despite vast disparities in energy scales and interaction mechanisms between electron spins in DNP and nuclear singlet states in PHIP, a pseudo-spin formalism allows us to establish an intriguing equivalence. As a result, the important low-field polarization transfer regime of PHIP can be mapped onto an analogous system equivalent to pulsed-DNP. This establishes a correspondence between key polarization transfer sequences in PHIP and DNP, facilitating the transfer of sequence development concepts. This promises fresh insights and significant cross-pollination between DNP and PHIP polarization sequence developers.

quant-ph↗

arXiv2023

A Viscous and Heat Conducting Ghost Fluid Method for Multi-Fluid Simulations

The ghost fluid method allows a propagating interface to remain sharp during a numerical simulation. The solution of the Riemann problem at the interface provides proper information to determine interfacial fluxes as well as the velocity of the phase boundary. Then considering two-material problems, the initial states of the Riemann problem belong to different fluids, which may have different equations of states. In the inviscid case, the solution of the multi-fluid Riemann problem is an extension of the classical Riemann problem for a single fluid. The jump of the initial states between different fluids generates waves in both fluids and induces a movement of the interface, similar to a contact discontinuity. More subtle is the extension to viscous and heat conduction terms which is the main focus of this paper. We account for the discontinuous coefficients of viscosity and heat conduction at the multi-fluid interface and derive solutions of the Riemann problem for these parabolic terms, from which we can derive parabolic, interfacial fluxes.

physics.flu-dyn↗

arXiv2022

Radio-Frequency Sweeps at μT Fields for Parahydrogen-Induced Polarization of Biomolecules

Magnetic resonance imaging of $^{13}$C-labeled metabolites enhanced by parahydrogen-induced polarization (PHIP) can enable real-time monitoring of processes within the body. We introduce a robust, easily implementable technique for transferring parahydrogen-derived singlet order into 13C magnetization using adiabatic radio-frequency sweeps at $μ$T fields. We experimentally demonstrate the applicability of this technique to several molecules, including some molecules relevant for metabolic imaging, where we show significant improvements in the achievable polarization, in some cases reaching above 60%. Furthermore, we introduce a site-selective deuteration scheme, where deuterium is included in the coupling network of a pyruvate ester to enhance the efficiency of the polarization transfer. These improvements are enabled by the fact that the transfer protocol avoids relaxation induced by strongly coupled quadrupolar nuclei.

physics.chem-ph↗

arXiv2022

Macroscopic Hyperpolarization Enhanced with Quantum Optimal Control

Hyperpolarization of nuclear spins enhances nuclear magnetic resonance signals, which play a key role for imaging and spectroscopy in the natural and life sciences. This signal amplification unlocks previously inaccessible techniques, such as metabolic imaging of cancer cells. In this work, electron spins from the photoexcited triplet state of pentacene-doped naphthalene crystals are used to polarize surrounding protons. As existing strategies are rendered less effective by experimental constraints, they are replaced with optimal control pulses designed with RedCRAB. In contrast to previous optimal control approaches, which consider an average single nucleus, this closed-loop optimization is macroscopic. A 28% improvement in signal and 15% faster polarization rate is observed. Additionally, a strategy called Autonomously-optimized Repeated Linear Sweep (ARISE) is introduced to efficiently tailor existing hyperpolarization sequences in the presence of experimental uncertainty to enhance their performance. ARISE is expected to be broadly applicable in many experimental settings.

quant-ph↗

arXiv2021

Hyperpolarized solution-state NMR spectroscopy with optically polarized crystals

Nuclear spin hyperpolarization provides a promising route to overcome the challenges imposed by the limited sensitivity of nuclear magnetic resonance. Here we demonstrate that dissolution of spin-polarized pentacene-doped naphthalene crystals enables transfer of polarization to target molecules via intermolecular cross relaxation at room temperature and moderate magnetic fields (1.45$\,$T). This makes it possible to exploit the high spin polarization of optically polarized crystals while mitigating the challenges of its transfer to external nuclei, particularly of the large distances and prohibitively weak coupling between source and target nuclei across solid-solid or solid-liquid interfaces. With this method, here we inject the highly polarized mixture into a benchtop NMR spectrometer and observe the polarization dynamics for target $^1$H nuclei. Although the spectra are radiation damped due to the high naphthalene magnetization, we describe a procedure to process the data in order to obtain more conventional NMR spectra, and extract the target nuclei polarization. With the entire process occurring on a timescale of one minute, we observe NMR signals enhanced by factors between -200 and -1730 at 1.45$\,$T for a range of small molecules.

physics.chem-ph↗

arXiv2021

Scaling Polyhedral Neural Network Verification on GPUs

Certifying the robustness of neural networks against adversarial attacks is essential to their reliable adoption in safety-critical systems such as autonomous driving and medical diagnosis. Unfortunately, state-of-the-art verifiers either do not scale to bigger networks or are too imprecise to prove robustness, limiting their practical adoption. In this work, we introduce GPUPoly, a scalable verifier that can prove the robustness of significantly larger deep neural networks than previously possible. The key technical insight behind GPUPoly is the design of custom, sound polyhedra algorithms for neural network verification on a GPU. Our algorithms leverage the available GPU parallelism and inherent sparsity of the underlying verification task. GPUPoly scales to large networks: for example, it can prove the robustness of a 1M neuron, 34-layer deep residual network in approximately 34.5 ms. We believe GPUPoly is a promising step towards practical verification of real-world neural networks.

cs.LG↗

arXiv2021

Top-pair production via gluon fusion in the Standard Model Effective Field Theory

We compute the leading corrections to the differential cross section for top-pair production via gluon fusion due to dimension-six operators at leading order in QCD. The Standard Model fields are assumed to couple only weakly to the hypothetical new sector. A systematic approach then suggests treating single insertions of the operator class containing gluon field strength tensors on the same footing as expli\-citly loop suppressed contributions from four-fermion operators. This is in particular the case for the chromomagnetic operator $Q_{(uG)}$ and the purely bosonic operators $Q_{(G)}$ and $Q_{(φG)}$. All leading order dimension-six contributions are consequently suppressed with a loop factor $1/16π^2$.

hep-ph↗

arXiv2020

Domain-Specific Multi-Level IR Rewriting for GPU

Traditional compilers operate on a single generic intermediate representation (IR). These IRs are usually low-level and close to machine instructions. As a result, optimizations relying on domain-specific information are either not possible or require complex analysis to recover the missing information. In contrast, multi-level rewriting instantiates a hierarchy of dialects (IRs), lowers programs level-by-level, and performs code transformations at the most suitable level. We demonstrate the effectiveness of this approach for the weather and climate domain. In particular, we develop a prototype compiler and design stencil- and GPU-specific dialects based on a set of newly introduced design principles. We find that two domain-specific optimizations (500 lines of code) realized on top of LLVM's extensible MLIR compiler infrastructure suffice to outperform state-of-the-art solutions. In essence, multi-level rewriting promises to herald the age of specialized compilers composed from domain- and target-specific dialects implemented on top of a shared infrastructure.

cs.PL↗

arXiv2020

A novel regularization strategy for the local discontinuous Galerkin method for level-set reinitialization

In this paper we propose a novel regularization strategy for the local discontinuous Galerkin method to solve the Hamilton-Jacobi equation in the context of level-set reinitialization. The novel regularization idea works in analogy to shock-capturing schemes for discontinuous Galerkin methods, which are based on finite volume sub-cells. In this spirit, the local discontinuous Galerkin method is combined with an upwind/downwind finite volume sub-cell discretization, which is applied in areas of low regularity. To ensure the applicability on unstructured meshes, the finite volume discretization is based on a least squares approach.

math.NA↗

arXiv2020

A solver for stiff finite-rate relaxation in Baer-Nunziato two-phase flow models

In this paper we present a technique for constructing robust solvers for stiff algebraic source terms, such as those typically used for modelling relaxation processes in hyperbolic systems of partial differential equations describing two-phase flows, namely models of the Baer-Nunziato family. The method is based on an exponential integrator which employs an approximate linearised source term operator that is constructed in such a way that one can compute solutions to the linearised equations avoiding any delicate matrix inversion operations.

math.NA↗

arXiv2017

Unambiguous nuclear spin detection using engineered quantum sensing sequence

Sensing, localising and identifying individual nuclear spins or frequency components of a signal in the presence of a noisy environments requires the development of robust and selective methods of dynamical decoupling. An important challenge that remains to be addressed in this context are spurious higher order resonances in current dynamical decoupling sequences as they can lead to the misidentification of nuclei or of different frequency components of external signals. Here we overcome this challenge with engineered quantum sensing sequences that achieve both, enhanced robustness and the simultaneous suppression of higher order harmonic resonances. We demonstrate experimentally the principle using a single nitrogen-vacancy center spin sensor which we apply to the unambiguous detection of external protons.

quant-ph↗