Once again we're at a stopping point because of our inability to effectively predict MAE. Our AI discovery agents have discovered materials that have all the properties we can currently predict. This is great and we should keep these going, however we aren't able to distinguish between a hard and soft magnet in the search/evolution process.
Today I will be working on coming up with a solution to this.
Two approaches I'm thinking about:
Figure out some AWS service that will let me set up containers that can scale to zero and use the specialized hardware that our HamGNN + TB2J approach takes.
Or, find a way to make this implementation not dependent on the specialized hardware (recompile scripts).
Use the datasets we've collected and train a GNN model to predict MAE.
Check out a lot of the prior work here:
Like our work on Curie temperature, the effort here is to build a machine learning model that can take a crystal structure and predict its magnetocrystalline anisotropy energy. Relevant for permanent
Far more successful this time! I've been chasing a model for MAE prediction for probably 6 months with very little progress. Coming to materials science with my background, DFT was always something ju
Working on cleaning the data we have available and seeing what we've got for a MAE prediction model. This resource was nice and had all the raw files uploaded so that you can process them yourself and
I'm leaning towards the figure out what we have and get that to work. That model is able to predict the Hamiltonian which is useful for understanding many other properties of a material. I'd love to be able to explore more API endpoints around that, so getting it running in a container and behind an API is a prerequisite.
Building a model is also very enticing, especially with how much I've learned since the last time I attempted it.
This time around I'd try to train a model from scratch such that the features the neural network builds/learns are specific to predicting MAE. That was a problem last time. Data availability and scale is still a problem though.