A two-body machine learning model based on Gaussian Approximation Potentials is trained to reproduce noncollinear DFT energies and fields for bcc Fe within 1 meV per spin under an adiabatic spin approximation.
but can extend prac- tically to about T > ΘD/3 in Pt, Cu and Au and to about T > ΘD/5 in Al” [99]
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Smooth Overlap of Spin Orientations: Machine Learning Exchange Fields for Ab-initio Spin Dynamics
A two-body machine learning model based on Gaussian Approximation Potentials is trained to reproduce noncollinear DFT energies and fields for bcc Fe within 1 meV per spin under an adiabatic spin approximation.