Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure
Abstract The prediction of corrosion resistance in High-entropy alloys (HEAs) faces challenges due to previous machine learning methods not fully capturing the interdependencies between composition, processing, and crystal structure. This study proposes the Composition and Processing-Driven Two-Stag...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00632-4 |
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| _version_ | 1849769122989080576 |
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| author | Guangxuan Song Dongmei Fu Yongjie Lin Lingwei Ma Dawei Zhang |
| author_facet | Guangxuan Song Dongmei Fu Yongjie Lin Lingwei Ma Dawei Zhang |
| author_sort | Guangxuan Song |
| collection | DOAJ |
| description | Abstract The prediction of corrosion resistance in High-entropy alloys (HEAs) faces challenges due to previous machine learning methods not fully capturing the interdependencies between composition, processing, and crystal structure. This study proposes the Composition and Processing-Driven Two-Stage Corrosion Prediction Framework with Structural Prediction (CPSP Framework), which first predicts crystal structure and then combines composition and processing data for corrosion current prediction. A deep learning model, Mat-NRKG, is developed based on the CPSP framework, efficiently integrating composition, processing, and crystal structure data through a knowledge graph and graph convolutional network. Evaluations using the HEA-CRD dataset show that the CPSP Framework outperforms the Composition-Only Prediction Framework (CP Framework) and the Composition and Processing-Based Prediction Framework (CPP Framework). The Mat-NRKG model demonstrates the best performance on the HEA-CRD dataset. Its generalization capability is validated through experiments on five laboratory-synthesized HEAs, highlighting the effectiveness of incorporating prior knowledge into model design for performance prediction. |
| format | Article |
| id | doaj-art-8bb4278d21684acc94cb93bc541c2b4c |
| institution | DOAJ |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-8bb4278d21684acc94cb93bc541c2b4c2025-08-20T03:03:34ZengNature Portfolionpj Materials Degradation2397-21062025-07-019111210.1038/s41529-025-00632-4Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structureGuangxuan Song0Dongmei Fu1Yongjie Lin2Lingwei Ma3Dawei Zhang4Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology BeijingBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology BeijingNational Materials Corrosion and Protection Data Center, University of Science and Technology BeijingNational Materials Corrosion and Protection Data Center, University of Science and Technology BeijingNational Materials Corrosion and Protection Data Center, University of Science and Technology BeijingAbstract The prediction of corrosion resistance in High-entropy alloys (HEAs) faces challenges due to previous machine learning methods not fully capturing the interdependencies between composition, processing, and crystal structure. This study proposes the Composition and Processing-Driven Two-Stage Corrosion Prediction Framework with Structural Prediction (CPSP Framework), which first predicts crystal structure and then combines composition and processing data for corrosion current prediction. A deep learning model, Mat-NRKG, is developed based on the CPSP framework, efficiently integrating composition, processing, and crystal structure data through a knowledge graph and graph convolutional network. Evaluations using the HEA-CRD dataset show that the CPSP Framework outperforms the Composition-Only Prediction Framework (CP Framework) and the Composition and Processing-Based Prediction Framework (CPP Framework). The Mat-NRKG model demonstrates the best performance on the HEA-CRD dataset. Its generalization capability is validated through experiments on five laboratory-synthesized HEAs, highlighting the effectiveness of incorporating prior knowledge into model design for performance prediction.https://doi.org/10.1038/s41529-025-00632-4 |
| spellingShingle | Guangxuan Song Dongmei Fu Yongjie Lin Lingwei Ma Dawei Zhang Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure npj Materials Degradation |
| title | Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure |
| title_full | Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure |
| title_fullStr | Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure |
| title_full_unstemmed | Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure |
| title_short | Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure |
| title_sort | corrosion resistance prediction of high entropy alloys framework and knowledge graph driven method integrating composition processing and crystal structure |
| url | https://doi.org/10.1038/s41529-025-00632-4 |
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