Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
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Abstract (arXiv)
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.
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References · 10
- 2016Gaussian Error Linear Units (GELUs)cited by 3,431
- 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
- 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 935
- 2017Self-Normalizing Neural Networkscited by 591
- 2019On Exact Computation with an Infinitely Wide Neural Netcited by 572
- 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 213
- 2017Machine Learning Molecular Dynamics for the Simulation of Infrared Spectracited by 30
- 2021Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentialscited by 15
- 2021Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Momentscited by 9
- 2018Machine learning enables long time scale molecular photodynamics simulationscited by 8
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