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

Good news! This is only the beginning of developing new materials in record time!

"... 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 around 400 structures that, they calculated, would have negative energy of formation. They then trained a DFT algorithm using data from previous experiments with ternary iron–cobalt compounds to predict the maximum magnetisations and the magnetic anisotropies of various structures. Finally, they used their adaptive genetic algorithm to generate new structures from the most interesting candidates. ...

picking out the most promising candidates then combining, optimising and calculating the new structures’ properties. ...

The researchers thereby arrived quickly at the most promising compounds without analysing every single combination of the three elements. 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,’ ..."

From the significance and abstract:

"Significance

Discovering rare earth (RE)–free magnets that can meet the performance and cost goals for advanced electromagnetic devices has been the dream of many scientists over several decades. We present the efficient discovery and synthesis of an RE-free magnetic Fe3CoB2 compound in this paper. Our machine learning (ML)–guided framework greatly reduces the complexity of high-throughput screening and makes ab initio calculations and further structure searches using an adaptive genetic algorithm much more effective than previous approaches. We demonstrate that our ML-guided framework enables the computational discovery and experimental synthesis of an Fe3CoB2 compound to be accomplished in days. ...
Abstract

... Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe3CoB2, through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe3CoB2 exhibits a high magnetic anisotropy (K1 = 1.2 MJ/m3) and saturation magnetic polarization (Js = 1.39 T), which is suitable for RE-free permanent-magnet applications. ..."

Powerful rare-earth free magnet ‘evolved’ and refined by machine learning algorithm | Research | Chemistry World Combination of iron, cobalt and boron points to alternatives to metals beset by geopolitical battles

Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback (no public access)