Machine Learning Spin Dynamics of Strongly Correlated Electron Systems

Puhan Zhang
In this dissertation, new numerical frameworks based on machine learning potentials to enable large-scale adiabatic quantum Landau-Lifshitz-Gilbert dynamics simulations of itinerant electron magnets are established. Such metallic spin systems are central to novel phenomena such as colossal magnetoresistance and spin-transfer torques. This approach is similar in spirit to the Behler-Parrinello machine learning scheme that has become a cornerstone of large-scale molecular dynamics method with the accuracy of quantum calculation. Based on the principle of locality...
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