SKM 2023 – wissenschaftliches Programm

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MA: Fachverband Magnetismus

MA 19: Molecular Magnetism I

MA 19.2: Vortrag

Dienstag, 28. März 2023, 15:15–15:30, HSZ 04

Machine learning based parameterization of magnetic data of single-molecule magnets — •Zayan Ahsan Ali, Julius Mutschler, and Oliver Waldmann — Physikalisches Institut, Universität Freiburg, D-79104 Freiburg, Germany

Single molecule magnets (SMMs) have attracted a rich volume of research in the last two decades due to their potential applications in magnetic memory and quantum computing. Lanthanide-based SMMs in particular demonstrate promising magnetic retention due to large inherent anisotropies. Their magnetic properties can be parameterized by ligand-field theories involving a set of 28 parameters. Experimental data such as magnetization and susceptibility curves, however, are typically featureless for these materials. Multiple distinct parameter sets can describe the data to equal accuracy, making it a formidable task to determine the model parameters for a compound. In this work, the over-parameterization is tackled by Machine Learning (ML) applied to data simulated for a single-ion model. For dimensionality reduction, a variational autoencoder is used to determine hidden system parameters of the data, and an invertible neural network is used to relate hidden parameters with the model parameters from ligand-field theory. The effectiveness of this ML model in producing consistent sets of ligand-field parameters for novel experimental data is investigated and presented.