Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning
Curve data are essential tools in materials science for characterizing material properties. However, obtaining and analyzing these curve data such as electrochemical corrosion curves to establish the intrinsic relationship of the material is time-consuming work. While machine learning (ML) method ca...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525006227 |
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| author | Xinpeng Zhao Haiyou Huang Yanjing Su Lijie Qiao Yu Yan |
| author_facet | Xinpeng Zhao Haiyou Huang Yanjing Su Lijie Qiao Yu Yan |
| author_sort | Xinpeng Zhao |
| collection | DOAJ |
| description | Curve data are essential tools in materials science for characterizing material properties. However, obtaining and analyzing these curve data such as electrochemical corrosion curves to establish the intrinsic relationship of the material is time-consuming work. While machine learning (ML) method can dramatically accelerate material research and development, accurately predicting electrochemical curves to understand the corrosion behavior of corrosion-resistant alloys remains a significant challenge, due to that macroscopic experiments and microscopic theoretical simulations have yet to be effectively integrated. In this work, we propose a data-driven method that integrates global and focused learning (GFL) strategies. Taking refractory high-entropy alloys (RHEAs) as a case study, we establish prediction models for their corrosion behavior based on potentiodynamic polarization curve data and interpretable GFL. Through compositional optimization, a series of RHEAs with high corrosion resistance, such as Ti20V10Nb20Mo20Ta30, have been obtained. This alloy exhibits excellent corrosion resistance compared with other RHEAs. In addition, compared with traditional single ML methods, GFL not only accurately predicted the polarization curves of RHEAs but also captures the key factors affecting the corrosion resistance of the alloys. The GFL strategy provides an effective ML tool with physical interpretation for material curve data analyzation. |
| format | Article |
| id | doaj-art-2bea8debfca34622957b8c982891ebc0 |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-2bea8debfca34622957b8c982891ebc02025-08-20T03:30:43ZengElsevierMaterials & Design0264-12752025-07-0125511420210.1016/j.matdes.2025.114202Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learningXinpeng Zhao0Haiyou Huang1Yanjing Su2Lijie Qiao3Yu Yan4Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, ChinaInstitute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China; Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China; Corresponding author at: Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China.Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China; Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaInstitute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China; Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaInstitute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China; Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China; Corresponding author at: Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China.Curve data are essential tools in materials science for characterizing material properties. However, obtaining and analyzing these curve data such as electrochemical corrosion curves to establish the intrinsic relationship of the material is time-consuming work. While machine learning (ML) method can dramatically accelerate material research and development, accurately predicting electrochemical curves to understand the corrosion behavior of corrosion-resistant alloys remains a significant challenge, due to that macroscopic experiments and microscopic theoretical simulations have yet to be effectively integrated. In this work, we propose a data-driven method that integrates global and focused learning (GFL) strategies. Taking refractory high-entropy alloys (RHEAs) as a case study, we establish prediction models for their corrosion behavior based on potentiodynamic polarization curve data and interpretable GFL. Through compositional optimization, a series of RHEAs with high corrosion resistance, such as Ti20V10Nb20Mo20Ta30, have been obtained. This alloy exhibits excellent corrosion resistance compared with other RHEAs. In addition, compared with traditional single ML methods, GFL not only accurately predicted the polarization curves of RHEAs but also captures the key factors affecting the corrosion resistance of the alloys. The GFL strategy provides an effective ML tool with physical interpretation for material curve data analyzation.http://www.sciencedirect.com/science/article/pii/S0264127525006227Machine learningCurve dataGlobal and focused learningRefractory high-entropy alloysCorrosion resistance |
| spellingShingle | Xinpeng Zhao Haiyou Huang Yanjing Su Lijie Qiao Yu Yan Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning Materials & Design Machine learning Curve data Global and focused learning Refractory high-entropy alloys Corrosion resistance |
| title | Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning |
| title_full | Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning |
| title_fullStr | Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning |
| title_full_unstemmed | Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning |
| title_short | Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning |
| title_sort | data driven electrochemical behavior prediction for refractory high entropy alloys by global and focused learning |
| topic | Machine learning Curve data Global and focused learning Refractory high-entropy alloys Corrosion resistance |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525006227 |
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