HomeTechnologyArtificial intelligencePowerful rare-earth free magnet ‘evolved’ and refined by machine learning algorithm |...

Powerful rare-earth free magnet ‘evolved’ and refined by machine learning algorithm | Research

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-led approach. The material needs further development, but the demonstration is an important step towards making powerful magnets that do not rely on rare earth metals.

Permanent magnets are critical to electricity generation in hydropower, wind power and a host of other green energy technologies, as well as information technologies. These devices 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. “Until now, the high-anisotropy magnets contained rare-earth metals,” says Cai Zhuang Wang from the U.S. Department of Energy’s Ames Laboratory at Iowa State University. “Why is a very fundamental question that is not yet fully understood.” Regardless of the mechanism, demand for permanent magnets will grow as society takes steps to reduce emissions by electrifying transportation and industry. There will therefore be a great demand for magnets made of cheap elements such as iron.

A material can only exhibit good magnetic anisotropy if it has an anisotropic lattice structure, which rare earths often do. However, iron-cobalt alloys are usually 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. However, they have often found that the structures are insufficiently stable and decompose at high temperatures.

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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.” They started with about 400 structures that, they calculated, would have negative energy of formation. They then trained a DFT algorithm using data from previous experiments with iron-cobalt ternary compounds to predict the maximum magnetizations and the magnetic anisotropies of various structures. Finally, they used their adaptive genetic algorithm to generate new structures from the most interesting candidates. “The simplest way is to take two structures and put them together as two parents,” explains Cai-Zhuang.

After each stage, the machine learning algorithm found the energetic ground states of their new structures by DFT and calculated the magnetic properties of these ground states, before using this data to improve subsequent predictions – picking the most promising candidates, then combining, optimizing and calculate the properties of the new structures. “It mimics the process of evolution,” explains Wang.

As a result, the researchers quickly arrived at the most promising compounds without analyzing every combination of the three elements. The researchers synthesized 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 much more complicated than a single crystal, so this just opens the door and there’s is 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, for example, don’t have their own reserve of rare earths, so this topic is very important,’ he says, ‘but it is also very difficult, because rare earths can have very high coercivity and also very high magnetization. I think it’s an important article.’

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