Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model
Abstract A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. By revealing the intrinsic drivers of strength, ductility, and corrosion resistanc...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00654-y |
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| _version_ | 1849764489455468544 |
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| author | Bingxiao Shi Xin Guo Luntao Wang Wenbo Huang Hong Luo Lizhi Qin Guowei Yang Xuequn Cheng Xiaogang Li |
| author_facet | Bingxiao Shi Xin Guo Luntao Wang Wenbo Huang Hong Luo Lizhi Qin Guowei Yang Xuequn Cheng Xiaogang Li |
| author_sort | Bingxiao Shi |
| collection | DOAJ |
| description | Abstract A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. By revealing the intrinsic drivers of strength, ductility, and corrosion resistance, and introducing a utility function to balance performance and cost, the newly designed steel achieved a UTS of 837 MPa, 20% elongation, and a corrosion rate of 0.54 g/(m2·h) after 576 hours in a simulated marine environment, demonstrating excellent overall properties. |
| format | Article |
| id | doaj-art-2881e8c2a0ee42a284a58df27e56ea69 |
| institution | DOAJ |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-2881e8c2a0ee42a284a58df27e56ea692025-08-20T03:05:07ZengNature Portfolionpj Materials Degradation2397-21062025-08-019111610.1038/s41529-025-00654-yMulti-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning modelBingxiao Shi0Xin Guo1Luntao Wang2Wenbo Huang3Hong Luo4Lizhi Qin5Guowei Yang6Xuequn Cheng7Xiaogang Li8Institute for Advanced Materials and Technology, University of Science and Technology BeijingInstitute for Advanced Materials and Technology, University of Science and Technology BeijingInstitute for Advanced Materials and Technology, University of Science and Technology BeijingSchool of Computer Science and Engineering, Southeast UniversityInstitute for Advanced Materials and Technology, University of Science and Technology BeijingInstitute for Advanced Materials and Technology, University of Science and Technology BeijingInstitute for Advanced Materials and Technology, University of Science and Technology BeijingInstitute for Advanced Materials and Technology, University of Science and Technology BeijingInstitute for Advanced Materials and Technology, University of Science and Technology BeijingAbstract A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. By revealing the intrinsic drivers of strength, ductility, and corrosion resistance, and introducing a utility function to balance performance and cost, the newly designed steel achieved a UTS of 837 MPa, 20% elongation, and a corrosion rate of 0.54 g/(m2·h) after 576 hours in a simulated marine environment, demonstrating excellent overall properties.https://doi.org/10.1038/s41529-025-00654-y |
| spellingShingle | Bingxiao Shi Xin Guo Luntao Wang Wenbo Huang Hong Luo Lizhi Qin Guowei Yang Xuequn Cheng Xiaogang Li Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model npj Materials Degradation |
| title | Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model |
| title_full | Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model |
| title_fullStr | Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model |
| title_full_unstemmed | Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model |
| title_short | Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model |
| title_sort | multi objective optimization of corrosion resistance strength ductility properties of weathering steel utilizing interpretable attention based deep learning model |
| url | https://doi.org/10.1038/s41529-025-00654-y |
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