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

• URL: http://arxiv.org/abs/2312.01415v1
• Date: Sun, 3 Dec 2023 14:37:57 GMT
• Title: Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
• Authors: Viktor Zaverkin, Julia Netz, Fabian Zills, Andreas K\"ohn, and Johannes K\"astner
• Abstract summary: We propose a machine learning method to model molecular tensorial quantities, namely the magnetic anisotropy tensor. 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.
• Score: 0.6116681488656472
• License: http://creativecommons.org/licenses/by/4.0/
• 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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