Machine Learning Models in Metals

Dear Colleagues,

Computational methods and simulations have greatly contributed to our understanding of the properties and behavior of metals. The integration of machine learning models, particularly neural networks, into the simulation of metal processes represents a significant stride forward in the field. In manufacturing, machine learning algorithms can optimize production processes by analyzing vast datasets in real-time, leading to increased efficiency and cost savings. Moreover, machine learning-driven material discovery has yielded exciting results, with algorithms identifying novel metal alloys with tailored properties for specific applications, such as lightweight yet strong materials for the aerospace industry. Additionally, characterizing complex microstructures and grain boundaries in metals has become more precise and efficient with neural networks, enabling researchers to better understand the relationship between microstructure and material performance. Overall, machine learning is transforming metallurgy, materials design, and numerous industries that rely on metals by accelerating innovation and enabling data-driven decision-making.

We invite researchers, scientists, and experts in the field to contribute to this Special Issue of Metals, entitled "Machine Learning Models in Metals." This Special Issue aims to provide a platform for the dissemination of cutting-edge research, novel methodologies, and innovative applications that harness the power of machine learning in the study of metals.

Suggested themes and article types for submissions

In this Special Issue, original research articles and reviews are welcome. Papers for this Special Issue should address, but are not limited to, the following topics:

• Machine learning-driven material discovery for novel metal alloys;
• Predictive modeling of mechanical properties, including tensile strength, hardness, and ductility;
• Computational techniques for optimizing metal manufacturing processes;
• Predictive modeling of metal corrosion and degradation;
• Machine learning-based defect detection and quality control in metal production;
• Data-driven approaches to understand metal–metal and metal–environment interactions;
• Machine learning techniques for characterizing microstructures and grain boundaries in metals;
• Applications of neural networks, deep learning, and reinforcement learning in metallurgy;
• Data-driven insights into metal behavior under extreme conditions, such as high temperature or pressure.

Prof. Dr. Olivier Pantale
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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• artificial neural networks
• machine learning in metallurgy
• deep learning
• data-driven materials science
• manufacturing process modeling and simulation
• metal alloy simulations
• metal properties modeling
• metal structure prediction
• computational materials science
• metal property prediction models