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

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.

References in corpus (4)

• Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
• Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
• Neural-Network Assisted Study of Nitrogen Atom Dynamics on Amorphous Solid Water. I. Adsorption & Desorption
• Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments

Cited by in corpus (5)

• Predicting tensorial molecular properties with equivariant machine-learning models
• Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
• Ab initio machine learning of phase space averages
• A deep learning model for chemical shieldings in molecular organic solids including anisotropy
• Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials