Machine learning-enabled design of Fe-based bulk metallic glasses for superior thermal neutron absorption properties
文献类型：期刊论文
| 作者 | Gao, Jin1,2; Hou, Jianxin3; Wu, Yuting1; Ji, Baoting2; Wang, Debin2; Qiu, Keqiang1; You, Junhua1; Wang, Jianqiang2 |
| 刊名 | JOURNAL OF ALLOYS AND COMPOUNDS
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| 出版日期 | 2025-01-05 |
| 卷号 | 1010页码:10 |
| 关键词 | Bulk metallic glasses Machine learning Nuclear energy Glass-forming ability Neutron absorption |
| ISSN号 | 0925-8388 |
| DOI | 10.1016/j.jallcom.2024.177595 |
| 通讯作者 | Hou, Jianxin(jxhou@lam.ln.cn) ; Qiu, Keqiang(kqqiu@sut.edu.cn) ; Wang, Jianqiang(jqwang@imr.ac.cn) |
| 英文摘要 | The pressing demand for innovative Fe-based amorphous alloys that excel in both glass-forming ability (GFA) and neutron-absorption has led to the exploration of novel alloying concepts incorporating high levels of B and Gd. In this study, we utilized AutoGluon, an advance autoML framework, to pinpoint the optimal feature sets for predicting the Dmax in Fe-based bulk metallic glasses (BMGs), effectively excluding characteristic temperatures from our analysis. This approach was validated across a dataset of 241 data points, achieving an R2 of 0.817 and an MSE of 1.88. Further, we applied the SHAP method to determine critical conditions that enhance GFA, aligning these with the feature distribution of the extrapolated BMGs. Consequently, we successfully fabricated the alloy (Fe0.72B0.22Nb0.04Cr0.02)96.5Gd3.5, which not only reached a Dmax of 3 mm but also exhibited superior neutron absorption properties. This research enhances our understanding of GFA and supports the development of innovative Fe-based BMGs with optimized material properties. |
| 资助项目 | National Natural Science Foundation of China[U1908219] ; National Natural Science Foundation of China[52171163] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-CN-2021-2-2] ; Liaoning Applied Basic Research Program[2023JH2/101300011] ; Basic scientific research project of Liaoning Province Department of Education[LJKZZ20220024] ; Shenyang Science and Technology Project[23-407-3-13] |
| WOS研究方向 | Chemistry ; Materials Science ; Metallurgy & Metallurgical Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001361340400001 |
| 出版者 | ELSEVIER SCIENCE SA |
| 资助机构 | National Natural Science Foundation of China ; Key Research Program of the Chinese Academy of Sciences ; Liaoning Applied Basic Research Program ; Basic scientific research project of Liaoning Province Department of Education ; Shenyang Science and Technology Project |
| 源URL |  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Hou, Jianxin; Qiu, Keqiang; Wang, Jianqiang |
| 作者单位 | 1.Shenyang Univ Technol, Sch Mat Sci & Engn, Shenyang 110870, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 3.Liaoning Acad Mat, Inst Mat Intelligent Technol, Shenyang 110004, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Jin,Hou, Jianxin,Wu, Yuting,et al. Machine learning-enabled design of Fe-based bulk metallic glasses for superior thermal neutron absorption properties[J]. JOURNAL OF ALLOYS AND COMPOUNDS,2025,1010:10. |
| APA | Gao, Jin.,Hou, Jianxin.,Wu, Yuting.,Ji, Baoting.,Wang, Debin.,...&Wang, Jianqiang.(2025).Machine learning-enabled design of Fe-based bulk metallic glasses for superior thermal neutron absorption properties.JOURNAL OF ALLOYS AND COMPOUNDS,1010,10. |
| MLA | Gao, Jin,et al."Machine learning-enabled design of Fe-based bulk metallic glasses for superior thermal neutron absorption properties".JOURNAL OF ALLOYS AND COMPOUNDS 1010(2025):10. |
入库方式： OAI收割
来源：金属研究所
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