That's the mission here. The process is pretty simple. Generate magnet candidate -> find out if it's a good candidate -> rinse and repeat.
Anyone can contribute. It's a numbers game, so the more people contribute the better chance we have at finding something good.
In service of the mission, I've been working on adding some APIs to help with crystal generation and property prediction. The Python SDK is in a good enough place that we can start orchestrating these tools with code.
Generate a batch of candidate inorganic crystal structures biased toward a target DFT magnetic density. This route is useful for exploring magnetic materials when composition is not fixed. Returns a ZIP archive of CIF files; controls sample count and strengthens or relaxes the property conditioning.
Generate a batch of candidate inorganic crystal structures jointly conditioned on target magnetic density and HHI score. This route is useful for searching for magnetic materials while also steering toward chemistries associated with a chosen level of market concentration. Returns a ZIP archive of CIF files.
Each of these models outputs either a CIF file or a .zip file with a set of CIFs. You can use these CIF files as input to the property prediction models.
Sometimes these models output structures that are not at their relaxed ground state. This means there is internal tension in the structure that would not exist if allowed to relax (which would happen in real life).
Relax the structure and use the output as the new structure to continue testing.
Optimize atomic positions and (optionally) unit-cell parameters of a crystal structure using a machine learning interatomic potential. Upload a CIF file and receive the relaxed structure as a new CIF. Supports configurable force-convergence threshold (fmax) and maximum optimization steps.
Look for drastic changes in atomic positions or reduction in symmetry to get a sense of how good the initial generation was.
Before testing a candidate structure's properties, you first want to determine if would be possible to make in the lab. We can estimate a structure's stability in two ways.
Look for an energy above the hull less than 0.150 meV / atom. Any higher and it will be difficult to make.
Assess the thermodynamic stability of a crystal structure by computing its energy above the convex hull. The structure is first relaxed with an ML interatomic potential, then compared against the Materials Project phase diagram (with optional inclusion of previously computed phases on Ouro). Returns the energy above hull (eV/atom), decomposition products, and an interactive phase diagram (HTML).
Look for the absence of imaginary modes to get a sense of a material's dynamic stability.
Compute the phonon band structure of a crystal using the finite-displacement method with ML interatomic potential force constants. Upload a CIF file and receive a phonon dispersion plot (PNG) showing vibrational frequencies along high-symmetry paths in the Brillouin zone. Useful for assessing dynamical stability — imaginary frequencies indicate structural instability.
If the material passes both of these tests, you're ready to examine the material's properties.
Magnetic density tells us how strong a magnet is. Look for values 0.12 and above.
Curie temperature tells us at which temperature the magnet looses its ability to be a permanent magnet. Look for values above 500 K.
I have a model for magnetocrystalline anisotropy energy, but currently it requires some specialized hardware making it hard to share with the platform. Send me a message or tag me with any CIFs you want tested and I'll be happy to.
If you've made it this far and your material has passed all the tests, congratulations! I have yet to see any candidate make it this far but I know they're out there. Comment on this post with the CIF and we'll go from there!
Happy building team 🫡
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