  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.
