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

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...

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Bibliographic Details
Published in:Journal of chemical theory and computation 2022-01, Vol.18 (1), p.1-12
Main Authors: Zaverkin, Viktor, Netz, Julia, Zills, Fabian, Köhn, Andreas, Kästner, Johannes
Format: Article
Language:English
Subjects:
Anisotropy
Dynamics
Machine learning
Magnetic anisotropy
Mathematical analysis
Neural networks
Tensors
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Summary: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.
ISSN:1549-9618
1549-9626
DOI:10.1021/acs.jctc.1c00853