Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys
The efficient exploration of expansive material spaces remains a significant challenge in materials science. To address this issue, autonomous material search methods that combine machine learning with ab initio calculations have emerged as a promising solution. These approaches offer a systematic a...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Science and Technology of Advanced Materials: Methods |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27660400.2025.2470114 |
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| _version_ | 1849432065339031552 |
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| author | Yuma Iwasaki Daisuke Ogawa Masato Kotsugi Yukiko K. Takahashi |
| author_facet | Yuma Iwasaki Daisuke Ogawa Masato Kotsugi Yukiko K. Takahashi |
| author_sort | Yuma Iwasaki |
| collection | DOAJ |
| description | The efficient exploration of expansive material spaces remains a significant challenge in materials science. To address this issue, autonomous material search methods that combine machine learning with ab initio calculations have emerged as a promising solution. These approaches offer a systematic and rapid means of discovering new materials, particularly when the material space is too large. This requirement is particularly important in the development of L10-structured alloys as magnetic recording media. These materials require a high magnetic moment (M) and magnetocrystalline anisotropy energy (EMCA) to satisfy the demands of next-generation data storage technologies. Although autonomous search methods have been successfully applied to various material systems, quaternary L10 alloys with optimized magnetic properties remain an open and underexplored frontier. In this study, we present a simulation-based autonomous search method aimed at identifying quaternary L10 alloys with enhanced M and EMCA values. Over a continuous 100-day search, our system suggested the FeMnPtEr alloy system as a promising candidate, exhibiting superior values for both M and EMCA. Although further experimental validation is required, this study underscores the potential of autonomous search methods to accelerate the discovery of advanced materials. |
| format | Article |
| id | doaj-art-3ee4b7498ea24a65973bf7d120766f5e |
| institution | Kabale University |
| issn | 2766-0400 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials: Methods |
| spelling | doaj-art-3ee4b7498ea24a65973bf7d120766f5e2025-08-20T03:27:28ZengTaylor & Francis GroupScience and Technology of Advanced Materials: Methods2766-04002025-12-015110.1080/27660400.2025.2470114Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloysYuma Iwasaki0Daisuke Ogawa1Masato Kotsugi2Yukiko K. Takahashi3Center for Basic Research on Materials (CBRM), National Institute for Materials Science (NIMS), Tsukuba, JapanResearch Center for Magnetic and Spintronic Materials (CMSM), National Institute for Materials Science (NIMS), Tsukuba, JapanDepartment of Materials Science and Technology, Tokyo University of Science, Tokyo, JapanResearch Center for Magnetic and Spintronic Materials (CMSM), National Institute for Materials Science (NIMS), Tsukuba, JapanThe efficient exploration of expansive material spaces remains a significant challenge in materials science. To address this issue, autonomous material search methods that combine machine learning with ab initio calculations have emerged as a promising solution. These approaches offer a systematic and rapid means of discovering new materials, particularly when the material space is too large. This requirement is particularly important in the development of L10-structured alloys as magnetic recording media. These materials require a high magnetic moment (M) and magnetocrystalline anisotropy energy (EMCA) to satisfy the demands of next-generation data storage technologies. Although autonomous search methods have been successfully applied to various material systems, quaternary L10 alloys with optimized magnetic properties remain an open and underexplored frontier. In this study, we present a simulation-based autonomous search method aimed at identifying quaternary L10 alloys with enhanced M and EMCA values. Over a continuous 100-day search, our system suggested the FeMnPtEr alloy system as a promising candidate, exhibiting superior values for both M and EMCA. Although further experimental validation is required, this study underscores the potential of autonomous search methods to accelerate the discovery of advanced materials.https://www.tandfonline.com/doi/10.1080/27660400.2025.2470114L10FePtmachine learningab initio calculationsBayesian optimization |
| spellingShingle | Yuma Iwasaki Daisuke Ogawa Masato Kotsugi Yukiko K. Takahashi Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys Science and Technology of Advanced Materials: Methods L10 FePt machine learning ab initio calculations Bayesian optimization |
| title | Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys |
| title_full | Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys |
| title_fullStr | Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys |
| title_full_unstemmed | Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys |
| title_short | Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys |
| title_sort | autonomous materials search using machine learning and ab initio calculations for l10 fept based quaternary alloys |
| topic | L10 FePt machine learning ab initio calculations Bayesian optimization |
| url | https://www.tandfonline.com/doi/10.1080/27660400.2025.2470114 |
| work_keys_str_mv | AT yumaiwasaki autonomousmaterialssearchusingmachinelearningandabinitiocalculationsforl10feptbasedquaternaryalloys AT daisukeogawa autonomousmaterialssearchusingmachinelearningandabinitiocalculationsforl10feptbasedquaternaryalloys AT masatokotsugi autonomousmaterialssearchusingmachinelearningandabinitiocalculationsforl10feptbasedquaternaryalloys AT yukikoktakahashi autonomousmaterialssearchusingmachinelearningandabinitiocalculationsforl10feptbasedquaternaryalloys |