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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart material design via accelerated corrosion evaluation: convergence of high-throughput characterization and AI-driven prediction in marine engineering

Dianchun Ju, Yaqing Hou, Xiangyang Li, Fan Li et al.
2 citations
npj Materials Degradation
Machine Learning in Materials Science
1.04
article

Smart material design via accelerated corrosion evaluation: convergence of high-throughput characterization and AI-driven prediction in marine engineering

Dianchun Ju, Yaqing Hou, Xiangyang Li, Fan Li, Congcong Du, Siyu Xu
article en
2 citations

Abstract

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.

npj Materials DegradationVol. 9(1)
China Iron and Steel Research Institute Group (CN), Jiangsu University of Science and Technology (CN), Shenzhen Institute of Building Research (China) (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 21%
Machine Learning in Materials Science
1.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Smart material design via accelerated corrosion evaluation: convergence of high-throughput characterization and AI-driven prediction in marine engineering — Dianchun Ju, Yaqing Hou, et al. · npj Materials Degradation (2025) | TGRS Research Map | TGRS