Artificial Intelligence in Metallic Materials
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 254
Special Issue Editor
Interests: artificial intelligence; machine learning; new material design; microstructure; mechanical property
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advanced metallic materials, such as high-strength steels, advanced light alloys, high-temperature alloys and high-entropy alloys, are essential for modern engineering applications. However, the complexity of composition design, processing optimization and microstructure control poses significant challenges to traditional materials development approaches. In recent years, artificial intelligence (AI) and machine learning (ML) have become effective tools to address these challenges by enabling data-driven and knowledge-assisted materials research.
AI-based methods have shown great potential in the active design of advanced alloys, allowing efficient exploration of multi-dimensional composition and processing spaces. At the same time, intelligent microstructure analysis, supported by computer vision and deep learning, provides quantitative and automated characterization of phases, defects and morphological features. By integrating experimental data, simulation results and domain knowledge, machine learning models can further establish quantitative composition–process–microstructure–property relationships, offering new insights into structure–property correlations. In addition, AI-assisted approaches are increasingly applied to the prediction of service performance, such as fatigue, creep and damage evolution, contributing to improved lifetime assessment and reliability of metallic materials.
This Special Issue aims to collect original research articles, short communications and review papers on the application of Artificial Intelligence in Metallic Materials. Topics of interest include, but are not limited to, the following:
- AI- and ML-driven active design of advanced metallic materials, including high-strength steels, advanced light alloys, high-temperature alloys and high-entropy alloys
- Intelligent and quantitative analysis of microstructure, defects and damage evolution
- Machine learning models for composition–process–microstructure–property relationships
- AI-assisted prediction of fatigue, creep and other service-related properties
- Integration of data-driven approaches with physical metallurgy and materials modeling
We sincerely invite researchers to contribute their latest work to this Special Issue and to promote the development of intelligent metallic materials research.
Dr. Shasha Zhang
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- metallic materials
- artificial intelligence
- machine learning
- alloy design
- microstructure analysis
- composition–process–microstructure–property relationships
- fatigue and creep prediction
- data-driven materials science
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