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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| Format: | Article |
| Language: | English |
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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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| author | Atsufumi Hirohata Hiroki Koizumi Tufan Roy Masahito Tsujikawa Shigemi Mizukami Kenji Nawa Masafumi Shirai |
| author_facet | Atsufumi Hirohata Hiroki Koizumi Tufan Roy Masahito Tsujikawa Shigemi Mizukami Kenji Nawa Masafumi Shirai |
| author_sort | Atsufumi Hirohata |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6df2cdd8de984f09b95ba6227124ef2d |
| institution | Kabale University |
| issn | 2948-2119 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Spintronics |
| spelling | doaj-art-6df2cdd8de984f09b95ba6227124ef2d2025-08-20T04:01:42ZengNature Portfolionpj Spintronics2948-21192025-07-01311910.1038/s44306-025-00094-zMachine learning for the development of new materials for a magnetic tunnel junctionAtsufumi Hirohata0Hiroki Koizumi1Tufan Roy2Masahito Tsujikawa3Shigemi Mizukami4Kenji Nawa5Masafumi Shirai6Center for Science and Innovation in Spintronics, Tohoku UniversityCenter for Science and Innovation in Spintronics, Tohoku UniversityCenter for Science and Innovation in Spintronics, Tohoku UniversityResearch Institute of Electrical Communication, Tohoku UniversityCenter for Science and Innovation in Spintronics, Tohoku UniversityGraduate School of Engineering, Mie UniversityCenter for Science and Innovation in Spintronics, Tohoku UniversityAbstract 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.https://doi.org/10.1038/s44306-025-00094-z |
| spellingShingle | Atsufumi Hirohata Hiroki Koizumi Tufan Roy Masahito Tsujikawa Shigemi Mizukami Kenji Nawa Masafumi Shirai Machine learning for the development of new materials for a magnetic tunnel junction npj Spintronics |
| title | Machine learning for the development of new materials for a magnetic tunnel junction |
| title_full | Machine learning for the development of new materials for a magnetic tunnel junction |
| title_fullStr | Machine learning for the development of new materials for a magnetic tunnel junction |
| title_full_unstemmed | Machine learning for the development of new materials for a magnetic tunnel junction |
| title_short | Machine learning for the development of new materials for a magnetic tunnel junction |
| title_sort | machine learning for the development of new materials for a magnetic tunnel junction |
| url | https://doi.org/10.1038/s44306-025-00094-z |
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