Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty
Abstract We present a multi-objective Bayesian active learning strategy, which greatly accelerates the discovery of super high-strength and high-ductility lead-free solder alloys. The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01480-7 |
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| _version_ | 1841544469816541184 |
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| author | Qinghua Wei Yuanhao Wang Guo Yang Tianyuan Li Shuting Yu Ziqiang Dong Tong-Yi Zhang |
| author_facet | Qinghua Wei Yuanhao Wang Guo Yang Tianyuan Li Shuting Yu Ziqiang Dong Tong-Yi Zhang |
| author_sort | Qinghua Wei |
| collection | DOAJ |
| description | Abstract We present a multi-objective Bayesian active learning strategy, which greatly accelerates the discovery of super high-strength and high-ductility lead-free solder alloys. The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. Then, Bayesian sampling is applied to design the next experiments by balancing exploitation and exploration. Seven multi-objective active learning iterations discovered two novel super high-strength and high-ductility lead-free solder alloys. After that, various material characterizations were conducted on the two novel solder alloys, and the results exhibited their high performances in melting properties, wettability, electrical conductivity, and shear strength of the solder joint and explored the mechanism of high strength and high ductility of the alloys. The present work systematically analyzes the important role of experimental uncertainty in machine learning, especially in the global optimization for material design, which demands high generalizability of predictions. |
| format | Article |
| id | doaj-art-db2f1777837c41a7b85b9c375ee575e7 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-db2f1777837c41a7b85b9c375ee575e72025-01-12T12:32:18ZengNature Portfolionpj Computational Materials2057-39602025-01-0111111410.1038/s41524-024-01480-7Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertaintyQinghua Wei0Yuanhao Wang1Guo Yang2Tianyuan Li3Shuting Yu4Ziqiang Dong5Tong-Yi Zhang6Materials Genome Institute, Shanghai UniversityMaterials Genome Institute, Shanghai UniversityMaterials Genome Institute, Shanghai UniversityMaterials Genome Institute, Shanghai UniversityMaterials Genome Institute, Shanghai UniversityMaterials Genome Institute, Shanghai UniversityMaterials Genome Institute, Shanghai UniversityAbstract We present a multi-objective Bayesian active learning strategy, which greatly accelerates the discovery of super high-strength and high-ductility lead-free solder alloys. The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. Then, Bayesian sampling is applied to design the next experiments by balancing exploitation and exploration. Seven multi-objective active learning iterations discovered two novel super high-strength and high-ductility lead-free solder alloys. After that, various material characterizations were conducted on the two novel solder alloys, and the results exhibited their high performances in melting properties, wettability, electrical conductivity, and shear strength of the solder joint and explored the mechanism of high strength and high ductility of the alloys. The present work systematically analyzes the important role of experimental uncertainty in machine learning, especially in the global optimization for material design, which demands high generalizability of predictions.https://doi.org/10.1038/s41524-024-01480-7 |
| spellingShingle | Qinghua Wei Yuanhao Wang Guo Yang Tianyuan Li Shuting Yu Ziqiang Dong Tong-Yi Zhang Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty npj Computational Materials |
| title | Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty |
| title_full | Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty |
| title_fullStr | Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty |
| title_full_unstemmed | Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty |
| title_short | Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty |
| title_sort | discovering novel lead free solder alloy by multi objective bayesian active learning with experimental uncertainty |
| url | https://doi.org/10.1038/s41524-024-01480-7 |
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