So I spent most of the week trying to get the HamGNN + TB2J magnetocrystalline anisotropy energy predictor working.
Only to finally learn that the outputs of the pre-trained HamGNN model I was using did not even have the necessary physics.
I thought because it could predict SOC effects that we had everything we needed, but that was wrong.
We also need spin-polarization in our H0. This model was more likely designed to study SOC effects in non-magnetic materials which can still get you:
Band structure with SOC corrections
Topological invariants (Zā, Chern numbers)
Spin textures in k-space
Orbital angular momentum
Spin Hall conductivity
g-factors and effective masses
Optical properties with SOC
Use case: Non-magnetic materials, topological insulators without magnetism
I'm pretty bummed right now. I thought it was so close. I spent so much time and a lot of money trying to get it to work only to find out it wasn't possible going that round. Learned a lot though so at least we've got that.
So back to the drawing board. I found a couple new models predicting Hamiltonians like HamGNN.
Neither of these have pre-trained models unfortunately. So my next step might be to try to start collecting data we could use to train on of them. We need SOC and spin-polarization on! And I think all the data needs to come from the same DFT software, but we'll see on that.
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A post about trying to use HamGNN with TB2J to forecast magnetocrystalline anisotropy energy, only to find the pre-trained model lacks the needed physics. The main gap is the absence of spin-polarization in H0, making the model better suited for SOC in non-magnetic materials, not for magnetic predictions. Potential outputs still relevant to SOC include band structure corrections, topological invariants, spin textures in k-space, orbital angular momentum, spin Hall conductivity, g-factors, effective masses, and optical properties. The use case focuses on non-magnetic materials and topological insulators without magnetism. Next steps involve exploring new Hamiltonian models like DeepH-pack and MACE-H, noting they lack pre-trained models. The plan is to gather consistent data, ensure SOC and spin-polarization, and align data sources from the same DFT software. Links: https://github.com/mzjb/DeepH-pack, https://github.com/maurergroup/MACE-H.