Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints
Abstract Zinc (Zn) alloys offer advantages such as abundant resources and low cost. Nevertheless, their current mechanical properties limit application in more advanced fields. Due to the lack of clear compositional design methods, the development of high-performance Zn alloys is urgently needed. To...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01666-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849725068705267712 |
|---|---|
| author | Chenfeng Pan Wenwen Lin Jianxing Zhou Wei Jian Ka Chun Chan Yuk Lun Chan Lu Ren |
| author_facet | Chenfeng Pan Wenwen Lin Jianxing Zhou Wei Jian Ka Chun Chan Yuk Lun Chan Lu Ren |
| author_sort | Chenfeng Pan |
| collection | DOAJ |
| description | Abstract Zinc (Zn) alloys offer advantages such as abundant resources and low cost. Nevertheless, their current mechanical properties limit application in more advanced fields. Due to the lack of clear compositional design methods, the development of high-performance Zn alloys is urgently needed. To this end, this work proposes a fast and effective design strategy for Zn alloys based on machine learning (ML). The prediction models for the ultimate tensile strength, elongation, and hardness were successfully developed, with accuracies exceeding 90%. Interpretability analysis of the models was performed using the SHAP method with particle swarm optimization (PSO). Furthermore, a ML-based Zn alloy composition design system (ZACDS) was proposed by integrating the Bayesian optimization algorithm. A novel high-strength Zn alloy was successfully designed using ZACDS, demonstrating good agreement between predicted and experimental mechanical properties. This approach offers a new strategy for Zn alloy design under different compositional constraints and performance requirements. |
| format | Article |
| id | doaj-art-987fd38ab2ee4cab9cae68c7a682ec69 |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-987fd38ab2ee4cab9cae68c7a682ec692025-08-20T03:10:34ZengNature Portfolionpj Computational Materials2057-39602025-06-0111111110.1038/s41524-025-01666-7Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraintsChenfeng Pan0Wenwen Lin1Jianxing Zhou2Wei Jian3Ka Chun Chan4Yuk Lun Chan5Lu Ren6Faculty of Mechanical Engineering and Mechanics, Ningbo UniversityFaculty of Mechanical Engineering and Mechanics, Ningbo UniversityFaculty of Mechanical Engineering and Mechanics, Ningbo UniversityFaculty of Mechanical Engineering and Mechanics, Ningbo UniversityGenesis Alloys (Ningbo) LtdGenesis Alloys (Ningbo) LtdFaculty of Mechanical Engineering and Mechanics, Ningbo UniversityAbstract Zinc (Zn) alloys offer advantages such as abundant resources and low cost. Nevertheless, their current mechanical properties limit application in more advanced fields. Due to the lack of clear compositional design methods, the development of high-performance Zn alloys is urgently needed. To this end, this work proposes a fast and effective design strategy for Zn alloys based on machine learning (ML). The prediction models for the ultimate tensile strength, elongation, and hardness were successfully developed, with accuracies exceeding 90%. Interpretability analysis of the models was performed using the SHAP method with particle swarm optimization (PSO). Furthermore, a ML-based Zn alloy composition design system (ZACDS) was proposed by integrating the Bayesian optimization algorithm. A novel high-strength Zn alloy was successfully designed using ZACDS, demonstrating good agreement between predicted and experimental mechanical properties. This approach offers a new strategy for Zn alloy design under different compositional constraints and performance requirements.https://doi.org/10.1038/s41524-025-01666-7 |
| spellingShingle | Chenfeng Pan Wenwen Lin Jianxing Zhou Wei Jian Ka Chun Chan Yuk Lun Chan Lu Ren Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints npj Computational Materials |
| title | Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints |
| title_full | Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints |
| title_fullStr | Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints |
| title_full_unstemmed | Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints |
| title_short | Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints |
| title_sort | novel machine learning driven design strategy for high strength zn alloys optimization with multiple constraints |
| url | https://doi.org/10.1038/s41524-025-01666-7 |
| work_keys_str_mv | AT chenfengpan novelmachinelearningdrivendesignstrategyforhighstrengthznalloysoptimizationwithmultipleconstraints AT wenwenlin novelmachinelearningdrivendesignstrategyforhighstrengthznalloysoptimizationwithmultipleconstraints AT jianxingzhou novelmachinelearningdrivendesignstrategyforhighstrengthznalloysoptimizationwithmultipleconstraints AT weijian novelmachinelearningdrivendesignstrategyforhighstrengthznalloysoptimizationwithmultipleconstraints AT kachunchan novelmachinelearningdrivendesignstrategyforhighstrengthznalloysoptimizationwithmultipleconstraints AT yuklunchan novelmachinelearningdrivendesignstrategyforhighstrengthznalloysoptimizationwithmultipleconstraints AT luren novelmachinelearningdrivendesignstrategyforhighstrengthznalloysoptimizationwithmultipleconstraints |