arXiv subjects

Daniel Prestwood

Publications and source records attributed to Daniel Prestwood.

4 recordsLinked to original sources

arXiv2026

Magnetoelastic coupling in stripe-domain states of yttrium iron garnet

We study magnetoelastic coupling in stripe-domain magnetic states of $3\,\mathrm{\mu m}$-thick YIG thin films grown on a GGG substrate. Broadband ferromagnetic resonance reveals low-frequency stripe-domain magnon branches modulated by a field-independent phonon comb with a frequency spacing of $3.5\,\mathrm{MHz}$, matching the value predicted for confined thickness-shear modes of the GGG substrate. Analytical fitting yields coupling rates that vary between $0.33$--$0.54\,\mathrm{MHz}$ and cooperativities of order $10^{-1}$, indicating that the system is in the weak-coupling regime without resolvable avoided-crossing gaps. Magnon-phonon mode-overlap calculations using finite-element simulations show that the weak coupling arises from phase and domain-sign cancellation: the local magnetoelastic coupling is sizable, but more than $99\%$ of the coherent overlap cancels across the stripe texture. Fully coupled simulations further demonstrate phonon-mediated excitation of a remote YIG layer and show that efficient propagating-phonon generation requires spatially asymmetric magnon modes, establishing magnetic texture as a control parameter for magnon--phonon coupling.

cond-mat.mes-hall↗

arXiv2026

Dynamical stability by spin transfer in nearly isotropic magnets

Spin transfer torques (STTs) control magnetisation by electric currents, enabling a range of nano-scale spintronic applications. They can destabilise the equilibrium magnetisation state by counteracting magnetic relaxation. Here, we maximise the STT effect through a dedicated growth-annealing protocol for CoFeB thin films, such that magnetic anisotropies originating from the interface and shape almost cancel each other. The nearly isotropic magnets enable low-current dynamical stabilisation of the magnetisation in the direction opposite to an applied magnetic field, thereby realising a spintronic analogue of the Kapitza pendulum. In an intermediate current regime, the STT drives large magnetisation vector fluctuations that cover the entire Bloch sphere. The continuous variable associated with the stochastic magnetisation direction may serve as a resource for probabilistic computing and neuromorphic hardware. Our results establish isotropic magnets as a platform to study as-yet-uncharted, far-from-equilibrium spin dynamics including anti-magnonics, with promising implications for unconventional computing paradigms.

cond-mat.mes-hall↗

arXiv2025

Spin wave resonance in yttrium iron garnet stripe domains

We study a thin film yttrium iron garnet sample that exhibits magnetic stripe domains due to a small perpendicular magnetic anisotropy. Using wide-field magneto-optic Kerr effect measurements we reveal the domain pattern evolution as a function of applied field and discuss the role of the cubic anisotropy in the domain formation. Rich magnon spectra are observed in the stripe domain states, with a range of excitation conditions providing distinct spectra. The measurements are interpreted using micromagnetic simulations to provide the spatial profiles of each resonance mode. We further simulate domain patterns and resonance spectra accounting for the cubic anisotropy,with good correlation to experiment. This study highlights how non-collinear magnetic domain structures can host complex resonant behaviour in a low-damping magnetic material, with potential use in future magnonic applications.

cond-mat.mtrl-sci↗

arXiv2024

PRCpy: A Python Package for Processing of Physical Reservoir Computing

Physical reservoir computing (PRC) is a computing framework that harnesses the intrinsic dynamics of physical systems for computation. It offers a promising energy-efficient alternative to traditional von Neumann computing for certain tasks, particularly those demanding both memory and nonlinearity. As PRC is implemented across a broad variety of physical systems, the need increases for standardised tools for data processing and model training. In this manuscript, we introduce PRCpy, an open-source Python library designed to simplify the implementation and assessment of PRC for researchers. The package provides a high-level interface for data handling, preprocessing, model training, and evaluation. Key concepts are described and accompanied by experimental data on two benchmark problems: nonlinear transformation and future forecasting of chaotic signals. Throughout this manuscript, which will be updated as a rolling release, we aim to facilitate researchers from diverse disciplines to prioritise evaluating the computational benefits of the physical properties of their systems by simplifying data processing, model training and evaluation.

cs.CE↗