Machine Learning in Metallic Materials Processing and Optimizing
A special issue of Metals (ISSN 2075-4701).
Deadline for manuscript submissions: 10 September 2025 | Viewed by 104
Special Issue Editor
Special Issue Information
Dear Colleagues,
Machine learning has the potential to lead the way in advancing materials research frontiers and could even incubate new research areas and directions. To date, the traditional materials research and development paradigm remains heavily dependent on experiences and “trial and error”, while ML can uncover the intricate intrinsic relationships in materials’ composition-structure-process-properties-performance using AI, advancing the development and application of new materials by means of built digital twin models and “digital trial and error”.
To date, high-entropy alloys research has emphasized systems based on late transition metals. Usually, ML models comprise three essential elements, namely a dataset, feature engineering, and an ML algorithm. ML has emerged as a powerful tool in materials science and materials design, driven in part by the advent of large materials datasets. Various ML models are used to predict materials properties and to achieve targeted searches in latent areas using generative models and optimizers.
Prof. Dr. Jinwu Xu
Guest Editor
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Keywords
- machine learning
- metallic materials
- generative models
- multi-objective optimization
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