Regensburg 2025 – scientific programme

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MA: Fachverband Magnetismus

MA 3: Magnonics I

MA 3.7: Talk

Monday, March 17, 2025, 11:00–11:15, H18

Machine learning tool for inelastic neutron scattering: The case of CrSBr — •Nihad Abuawwad¹, Yixuan Zhang², Hongbin Zhang², and Samir Lounis^1,3 — ¹Peter Grünberg Institut, Forschungszentrum Jülich, Jülich, Germany — ²Institute of Materials Science, Technical University Darmstadt, Darmstadt , Germany — ³Institute of Physics, University of Halle, Halle, Germany

Spin waves, or magnons, are fundamental excitations in magnetic materials that provide insights into their dynamic properties and interactions. Magnons are the building blocks of magnonics, which offer promising perspectives for data storage, and quantum computing. These excitations are typically measured through Inelastic Neutron Scattering (INS) techniques, which involve heavy and time-consuming measurements, data processing, and analysis based on various theoretical models. Here, we introduce a machine learning algorithm that integrates adaptive noise reduction and active learning sampling, which enables the restoration from minimal INS point data of spin wave information and the accurate extraction of magnetic parameters, including hidden interactions. Our findings, benchmarked against the magnon spectra of CrSBr, significantly enhance the efficiency and accuracy in addressing complex and noisy experimental measurements. This advancement offers a powerful machine-learning tool for research in magnonics and spintronics, which can also be extended to other characterization techniques at large facilities[1].

[1] Abuawwad N. arxiv:2407.04457 (2024)

Keywords: Magnons; Inelastic neutron scattering; Machine learning; CrSBr