Machine learning for the development of new materials for a magnetic tunnel junction
Abstract In materials science, we have been increasing the number of constituent elements in an alloy and compounds to improve their properties. For example, in magnetism and spintronics, ternary alloys, such as NdFeB and CoFeB have been developed and widely used in permanent magnets and memories/se...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | npj Spintronics |
| Online Access: | https://doi.org/10.1038/s44306-025-00094-z |
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| Summary: | Abstract In materials science, we have been increasing the number of constituent elements in an alloy and compounds to improve their properties. For example, in magnetism and spintronics, ternary alloys, such as NdFeB and CoFeB have been developed and widely used in permanent magnets and memories/sensors, respectively. It has now been considered to be a time to add more elements to further explore their horizon. For such a complicated development, a manual systematic study is no longer practical, leading to the utilisation of machine learning to predict a candidate. These candidates can then be additionally screened by ab initio calculations before experimental confirmation, which can be performed routinely. Additional use of quantum annealing may also broaden the adoptability of machine learning on the materials development. In this perspective, we plan to offer a standardised process for such a development with some requirements for improvement. |
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| ISSN: | 2948-2119 |