Machine Learning-Enabled Design and Optimization of Nanostructured Alloys
A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Synthesis, Interfaces and Nanostructures".
Deadline for manuscript submissions: 17 October 2025 | Viewed by 37
Special Issue Editor
Special Issue Information
Dear Colleagues,
The development of nanostructured alloys with tailored properties is critical for advancing applications in catalysis, energy storage, and high-performance materials. However, traditional trial-and-error approaches are often inefficient in optimizing complex nanostructures. Machine learning (ML) will accelerate the design and optimization of nanostructured alloys, integrating high-throughput computational screening with experimental validation. The key features, such as grain boundary stability, phase distribution, and defect interactions, are analyzed to guide nanostructural engineering. The ML-driven framework not only reduces experimental costs but also uncovers novel alloy designs. The case studies have highlighted ML’s accuracy and generalizability on nanocrystalline and nanoporous alloys. The Nanomaterials journal focuses on innovative synthesis and computational approaches in nanoscience, contributing to the rational development of advanced materials.
This Special Issue will focus on the synthesis, functionalization, characterization, chemical, and physical properties, application, theory, and modeling of sulfur-based nanostructured materials for secondary batteries. This Special Issue aims to provide a comprehensive overview of the recent and forthcoming progress in the field. This will help researchers working on rechargeable batteries to focus on waste production found in the literature.
We invite interested authors to submit their original experimental, theoretical, and review papers focusing on the subjects for inclusion in this Special Issue.
Prof. Dr. Hanqing Xu
Guest Editor
Manuscript Submission Information
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Keywords
- nanostructured alloys
- machine learning
- materials design
- computational optimization
- high-throughput screening
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