Complex Spin Hamiltonian Represented by Artificial Neural Network
This provides a new method for simulating complex magnetic phenomena in materials like skyrmions, addressing a bottleneck in magnetism research, though it is incremental as it builds on existing ML and spin Hamiltonian approaches.
The authors tackled the problem of modeling complex magnetic interactions in itinerant magnets, which lack explicit functional forms, by developing a hybrid spin Hamiltonian combining an explicit Heisenberg part with an implicit non-linear artificial neural network part, successfully reproducing artificial models and describing the itinerant magnetism of bulk Fe3GeTe2.
The effective spin Hamiltonian method is widely adopted to simulate and understand the behavior of magnetism. However, the magnetic interactions of some systems, such as itinerant magnets, are too complex to be described by any explicit function, which prevents an accurate description of magnetism in such systems. Here, we put forward a machine learning (ML) approach, applying an artificial neural network (ANN) and a local spin descriptor to develop effective spin potentials for any form of interaction. The constructed Hamiltonians include an explicit Heisenberg part and an implicit non-linear ANN part. Such a method successfully reproduces artificially constructed models and also sufficiently describe the itinerant magnetism of bulk Fe3GeTe2. Our work paves a new way for investigating complex magnetic phenomena (e.g., skyrmions) of magnetic materials.