# Machine Learning Assisted Optimization of SmFe12-based Alloys https://mdr.nims.go.jp/datasets/8095a264-c064-49d1-8949-230b2fb172ff ## File - [abstract.docx](https://mdr.nims.go.jp/filesets/18f0ae02-bab0-4ed5-9354-f0dc18effc38/download) ([Detail](https://mdr.nims.go.jp/filesets/18f0ae02-bab0-4ed5-9354-f0dc18effc38.md)) ## Id 8095a264-c064-49d1-8949-230b2fb172ff ## Local identifier ## Visibility open_to_public ## State published ## Created at 2024-10-13T04:57:06.373743Z ## Updated at 2024-10-16T23:30:37.398522Z ## Published at 2024-10-16T23:30:37.618816Z ## Doi https://doi.org/10.48505/nims.4857 ## First published url ## Date published ## Recorded date published ## Resource type conference_poster ## Manuscript type na ## Collection ## Title - title: Machine Learning Assisted Optimization of SmFe12-based Alloys title_type: original lang: en ## Description - description: In this work, we conducted machine learning on an experimental dataset of SmFe12-based alloys with ThMn12-type crystal structure. The dataset comprised 908 samples collected from articles and our own experiments on the alloys which were obtained either by mechanical alloying or by melt-spinning followed by heat treatment. The descriptor of each sample consisted of the chemical composition and synthesis details. The importance of the features and their correlations were analyzed, then two gradient boosting regressors were trained to predict coercivity and remanence as the main targets. Next, a large feature space of (Sm,Zr)x(Fe,Ti,V)100-x melt spun samples with annealing temperatures ranging from 923 to 1373 K was examined to define a Pareto front of the competing targets. Finally, we proposed a list of the most prospective alloys for an experimental validation, the results of which will be reported, as well as other details of the machine learning on the SmFe12-based isotropic alloys. description_type: abstract lang: eng ## Creator - name: BOLYACHKIN Anton role: author orcid: https://orcid.org/0000-0003-0420-1806 organization: National Institute for Materials Science department: International Center for Young Scientists ror: https://ror.org/026v1ze26 - name: SUBAGJA Toni role: author orcid: https://orcid.org/0009-0003-8810-8808 organization: National Institute for Materials Science department: Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group ror: https://ror.org/026v1ze26 - name: LI Jiangnan role: author organization: National Institute for Materials Science department: Research Center for Magnetic and Spintronic Materials/Magnetic Materials Analysis Group ror: https://ror.org/026v1ze26 - name: ZHANG Jiasheng role: author organization: National Institute for Materials Science department: Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group ror: https://ror.org/026v1ze26 - name: ASHOK KRISHNASWAMY Srinithi role: author orcid: https://orcid.org/0000-0001-6209-3837 organization: National Institute for Materials Science department: Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group ror: https://ror.org/026v1ze26 - name: ABE Taichi role: author orcid: https://orcid.org/0000-0002-5065-0939 organization: National Institute for Materials Science department: Research Center for Structural Materials/Materials Evaluation Field/Structural Thermodynamics Group ror: https://ror.org/026v1ze26 - name: TANG Xin role: author orcid: https://orcid.org/0000-0001-6762-6145 organization: National Institute for Materials Science department: Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group ror: https://ror.org/026v1ze26 - name: TOZMAN KARANIKOLAS Pelin role: author organization: National Institute for Materials Science department: Global Networking Division/International Center for Young Scientists ror: https://ror.org/026v1ze26 - name: KULESH Nikita role: author orcid: https://orcid.org/0000-0001-7046-2671 organization: National Institute for Materials Science department: Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group ror: https://ror.org/026v1ze26 - name: SEPEHRI AMIN Hossein role: author orcid: https://orcid.org/0000-0002-7856-7897 organization: National Institute for Materials Science department: Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group ror: https://ror.org/026v1ze26 - name: OHKUBO Tadakatsu role: author orcid: https://orcid.org/0000-0003-3548-1951 organization: National Institute for Materials Science department: Research Center for Magnetic and Spintronic Materials ror: https://ror.org/026v1ze26 - name: HONO Kazuhiro role: author orcid: https://orcid.org/0000-0001-7367-0193 organization: National Institute for Materials Science ror: https://ror.org/026v1ze26 ## Contact agent ## Publisher ## Managing organization ## Keyword - subject: Machine learning schema: not_defined - subject: Hard magnetic materials schema: not_defined - subject: SmFe12-based alloys schema: not_defined ## Rights - identifier: http://rightsstatements.org/vocab/InC/1.0/ ## Other identifier(s) ## Data origin - data_origin_type: other ## Embargo ## Journal ## Conference name: IEEE Magnetic Frontiers Conference  start_date: 2024-09-15 end_date: 2024-09-19 identifier: https://magneticfrontiers24.sciencesconf.org/ ## Related item ## Funding ## Instrument ## Instrument operator ## Instrument managing organization ## Measurement method ## Specimen ## Chemical composition ## Structure for specimen ## Structural feature for specimen ## Specific property for specimen ## Process for specimen treatment ## Computational method ## Energy level/transition state ## Software ## Custom property ## Fileset - id: 18f0ae02-bab0-4ed5-9354-f0dc18effc38 filename: abstract.docx content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document size: 22183 md5: 2b79a4bc70acefd1ad6df7e4139cfcc8 ## Thumbnail fileset_id: 18f0ae02-bab0-4ed5-9354-f0dc18effc38 filename: abstract.docx