This could be quite interesting, ML-guided framework enables the...

This could be quite interesting, ML-guided framework enables the computational discovery and experimental synthesis of a magnetic Fe3CoB2 compound, thus there is a possibility for cheaper rare-earth free magnets that potentially use more boron than NdFeB magnets.

Even if these Fe3CoB2 magnets don’t end up quite as strong as NdFeB magnets they could still see significant use due to cost and less environmental impact as rare-earth mining often comes with radioactive waste products.

Cobalt is often called a “blood-mineral” out of Congo but alternative sources do exist with both pure deposit and as a by-product of nickel mining. Australia is one of those alternative streams for cobalt.

Wogan, Tim. “Powerful Rare-Earth Free Magnet ‘Evolved’ and Refined by Machine Learning Algorithm.” Chemistry World, Chemistry World, 25 Nov. 2022, www.chemistryworld.com/news/powerful-rare-earth-free-magnet-evolved-and-refined-by-machine-learning-algorithm/4016605.article

A rare-earth free magnetic material with similar properties to the rare-earth magnets found in everything from wind turbines to computer hard drives has been discovered by US researchers using a machine learning-guided approach. The material requires further development, but the demonstration constitutes an important step on the road to creating powerful magnets that aren’t dependent on rare earth elements.

Permanent magnets used for the generation of electricity in hydropower, wind power, other green energy technologies and information technologies need strong magnets with high coercivity – a well-constrained magnetic field.

Making these requires a magnetic material with high magnetic anisotropy – a measure of the dependence of the magnetic moment on the angle of the lattice. ‘So far the magnets with high anisotropy have contained rare earths,’ says Cai-Zhuang Wang of the US Department of Energy’s Ames Laboratory at Iowa State University.

A material can only show good magnetic anisotropy if it has an anisotropic lattice structure, which rare-earths compounds often do. Iron–cobalt alloys, however, tend to be most stable in cubic structures. Researchers have tried to break this symmetry by adding a third element such as nitrogen to occupy the interstitial positions in the cubic lattice. They have often found, however, that the structures are insufficiently stable and decompose at high temperatures.

Wang and colleagues at the Ames Laboratory and elsewhere looked at compounds containing iron, cobalt and boron using a combination of machine learning, density functional theory (DFT) and an ‘adaptive genetic algorithm’.

The researchers synthesised the most promising candidate, and found good agreement with their predictions. ‘I think this is the first demonstration of a rare-earth free magnet that does have high anisotropy,’ says Wang, ‘but the real magnet will be a lot more complicated than a single crystal, so this just opens the door and there’s a lot of work to be done.’

Ziyuan Rao of the Max Planck Institute for Iron Research in Düsseldorf is intrigued. ‘Many small countries in Europe, say, don’t have their own supplies of rare-earth elements, so this topic is very important,’ he says, ‘but it’s also very difficult, because rare earth metals can have very high coercivity and also very high magnetisation. I think it’s a significant paper.’

Paper:
Xia, Weiyi, et al. “Accelerating the Discovery of Novel Magnetic Materials Using Machine Learning–Guided Adaptive Feedback.” Proceedings of the National Academy of Sciences, vol. 119, no. 47, Nov. 2022, https://doi.org/10.1073/pnas.2204485119