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Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
- Source :
- J. Chem. Theory Comput. 2022, 18, 1, 1--12
- Publication Year :
- 2023
-
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.
Details
- Database :
- arXiv
- Journal :
- J. Chem. Theory Comput. 2022, 18, 1, 1--12
- Publication Type :
- Report
- Accession number :
- edsarx.2312.01415
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1021/acs.jctc.1c00853