Smart material design via accelerated corrosion evaluation: convergence of high-throughput characterization and AI-driven prediction in marine engineering
Traditional trial-and-error methods have hindered the rapid development of corrosion-resistant materials in the field of marine engineering. This paper provides a comprehensive review of high-throughput characterization, efficient multi-factor evaluation, and artificial intelligence prediction for metallic materials in marine environments. By integrating these three parts synergistically, this paper presents an innovative paradigm for materials corrosion research, aimed at significantly accelerating the design of next-generation smart materials suitable for harsh marine environments.
Authors
- Dianchun Ju (ORCID: https://orcid.org/0000-0001-5550-3092)
- Yaqing Hou (ORCID: https://orcid.org/0009-0007-6994-9475)
- Xiangyang Li (ORCID: https://orcid.org/0000-0002-7594-0246)
- Fan Li (ORCID: https://orcid.org/0000-0001-7105-5365)
- Congcong Du (ORCID: https://orcid.org/0009-0004-1473-5259)
- Siyu Xu
Institutions
- China Iron and Steel Research Institute Group (CN)
- Jiangsu University of Science and Technology (CN)
- Shenzhen Institute of Building Research (China) (CN)
Publication Details
- Journal
- npj Materials Degradation
- Published
- 2025-11-07
- DOI
- https://doi.org/10.1038/s41529-025-00663-x
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- National Key Research and Development Program of China