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

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