Predicting the magnetic properties of materials
Permanent magnets used in electric cars and wind turbines currently contain rare earth metals. Reducing the amount of these elements in magnets is important, as mining them is harmful both to health and the environment. Researchers have now developed a new machine learning tool to assist in quickly and easily predicting the ferromagnetic crystal properties of novel material compositions.
Renewable energy is a key technology for the future. However, both electric cars and wind turbines require large and strong permanent magnets. The inherent problem is that high-performance magnetic materials contain 12 to 17 percent rare earth elements, chiefly neodymium and samarium, but also dysprosium and terbium. The source of these elements is, almost exclusively, China. In addition, miners who extract these raw materials usually work under health-hazardous conditions, and the process damages the environment. Not surprisingly, materials researchers have set their sights for years on finding alternatives to the rare earth metals in permanent magnets. On the whole, the standard method is "trial and error": which elemental compositions have worked well in the past, and which might work equally well in the future? Testing like this is a costly and time-consuming undertaking.
Collecting candidates using computer simulation
Researchers at the Fraunhofer Institute for Mechanics of Materials IWM in Freiburg are an alternative, more effective approach. "We have developed a high-throughput computer simulation method to systematically and rapidly test a large number of materials as candidates for permanent magnets," explains Dr. Johannes Möller, a research scientist in the Material Design business unit at Fraunhofer IWM. "Our method isn't to consider which particular percentage of manganese, cobalt or boron might be viable, but to let the computer simulate many conceivable variants." This combinatorial approach can filter out promising compositions to create a collection of reasonable theoretical candidates that can then be systematically investigated. This considerably narrows things down compared to conventional trial and error methods. "In principle, this approach is not restricted to magnetic properties, but can also be applied to other material properties," Möller says.