Initial screening of magnetic materials

We're starting to bring a few of the pieces together in our permanent magnet screening pipeline. In this post we'll look at how well we are able to filter out materials from a list of ~5000 ferro/ferrimagnetic materials from Materials Project.

Building property prediction models has been challenging. It's been hard to find good data for magnetic properties like anisotropy, coercivity, Curie temperature. Without access to supercomputing resources, we must find to access these properties without computing them ourselves via DFT or micro-magnetic simulations.

A quick refresher on what these properties tell us:

• Remanence (Br) - residual magnetization after removal of external field

• Coercivity (Hc) - resistance to demagnetization by external fields

• Energy product (BHmax) - overall magnetic energy storage capacity

• Curie temperature (Tc) - temperature at which magnetism is lost

• Magnetocrystalline anisotropy - preferential direction of magnetization

So far, we are able to compute/predict two properties fairly well. These approaches each have their own flaws and uncertainty, but for screening, ballpark estimates are usually good enough.

Curie temperature:

Magnetic density (often called magnetic flux density or B-field):

Magnetic density is calculated as the sum of magnetic moments per unit volume. Using CHGNet and cell volume, we can estimate this value. More specifically, the magnetic density B is related to:

B = μ₀(H + M)

Where:

• B is the magnetic flux density (measured in tesla or gauss)

• μ₀ is the permeability of free space

• H is the external magnetic field strength

• M is the magnetization of the material (the volume density of magnetic moments)

Dataset

By running the ~5000 materials through both of these property predictors, we can start to filter materials that fall outside of our desired ranges.

For Curie temperature, we want to see temperatures at least above 300K room temperature, but 500+ is more desirable for practical applications.

For magnetic density, we want to see values 0.13 and above. NdBF magnets are around 0.15.

Here's a raw dataset of those results. We map Materials Project ID to their estimated properties.

Next steps

We will continue to build property prediction models to that we can further narrow down this list of candidates. We're still missing anisotropy/coercivity which is essential in determining a good permanent magnet candidate.

Furthermore, we should account for material's composition, filtering out materials that are high in rare-earth metals and other costly (and other negative properties like pollutive) materials.