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 (Z₂, 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.

https://github.com/mzjb/DeepH-pack

https://github.com/maurergroup/MACE-H

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.