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arXiv · 2312.01415

Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments

Viktor Zaverkin·Julia Netz·Fabian Zills·Andreas Köhn·Johannes Kästner
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Abstract

We propose a machine learning method to model molecular tensorial quantities, namely the magnetic anisotropy tensor, based on the Gaussian-moment neural-network approach. We demonstrate that the proposed methodology can achieve an accuracy of 0.3--0.4 cm$^{-1}$ and has excellent generalization capability for out-of-sample configurations. Moreover, in combination with machine-learned interatomic potential energies based on Gaussian moments, our approach can be applied to study the dynamic behavior of magnetic anisotropy tensors and provide a unique insight into spin-phonon relaxation.

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Viktor Zaverkin, Julia Netz, Fabian Zills, Andreas Köhn, Johannes Kästner. 2023-12-03. Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments. https://doi.org/10.1021/acs.jctc.1c00853

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