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  4. Compositional optimization of hard-magnetic phases with machine-learning models
 
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2018
Journal Article
Title

Compositional optimization of hard-magnetic phases with machine-learning models

Abstract
Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML models to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models are obtained from a combinatorial high-throughput screening based on density-functional theory calculations. Our straightforward choice of describing the different configurations enables the subsequent use of the ML models for compositional optimization and thereby the prediction of promising substitutes of state-of-the-art magnetic materials like Nd2Fe14B with similar intrinsic hard-magnetic properties but a lower amount of critical rare-earth elements.
Author(s)
Möller, J.
Fraunhofer-Institut für Werkstoffmechanik IWM  
Körner, W.
Fraunhofer-Institut für Werkstoffmechanik IWM  
Krugel, G.
Fraunhofer-Institut für Werkstoffmechanik IWM  
Urban, D.F.
Fraunhofer-Institut für Werkstoffmechanik IWM  
Elsässer, C.
Fraunhofer-Institut für Werkstoffmechanik IWM  
Journal
Acta Materialia  
Open Access
DOI
10.1016/j.actamat.2018.03.051
Link
Link
Language
English
Fraunhofer-Institut für Werkstoffmechanik IWM  
Keyword(s)
  • permanent magnet

  • machine learning

  • density functional theory

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