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Meeting 2018 TMS Annual Meeting & Exhibition
Symposium Computational Materials Discovery and Optimization
Presentation Title A Materials-informatics Approach for Finding New Hard-magnetic Phases
Author(s) Johannes J. Möller, Georg Krugel, Wolfgang Körner, Daniel F. Urban, Christian Elsässer
On-Site Speaker (Planned) Johannes J. Möller
Abstract Scope Data-mining and machine-learning (ML) techniques play an increasingly important role in the discovery and development of new materials. In this contribution, we use kernel-based learning methods 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 were obtained by a combinatorial high-throughput screening (HTS) using density-functional theory calculations. For encoding the structural and chemical information of the HTS data in a machine-readable format, we use several existing and newly developed material descriptors and assess the predictive power of the ML models built with them. The accuracy of the ML models with an optimal choice of descriptor and model parameters enables the prediction of promising structure-composition combinations for substitutes of state-of-the-art magnetic materials like Nd2Fe14B – with similar intrinsic hard-magnetic properties but less amounts of critical rare-earth elements.
Proceedings Inclusion? Planned: Supplemental Proceedings volume


A Combined Experimental-computational Approach to Determining Nanoscale Structures
A Materials-informatics Approach for Finding New Hard-magnetic Phases
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