Microstructure Design of Materials via Machine Learning: Advantage, Challenges, Applications, and Perspectives

Dear Colleagues,

This Special Issue aims to gather the latest advances in the field of the microstructure design of materials using machine learning techniques. Machine learning has emerged as a powerful tool in optimizing material properties through data-driven approaches. It offers several advantages, including accelerated discovery, improved performance, and reduced fabrication time and cost.

This Special Issue intends to focus on the advantages, challenges, applications, and perspectives of the microstructure design of materials using machine learning. We are particularly interested in novel structures, algorithms, and methodologies that exploit the potential of machine learning to optimize material microstructures for achieving enhanced properties. Additionally, unconventional applications, such as measurement techniques and computational models, that contribute to microstructure development are also encouraged.

The collection aims to present recent and important results that will be beneficial for both young investigators and leading experts in the field. It will provide valuable insights and inspiration for researchers interested in the design and development of materials with improved properties using machine learning techniques.

We encourage submissions that explore various material systems and properties, as well as studies that highlight the potential and limitations of machine learning in the microstructure design of materials. We believe that this Special Issue will offer a comprehensive overview of the advantages, challenges, and future perspectives of material design through machine learning.

Dr. Sugrib K. Shaha
Dr. Dyuti Sarker
Guest Editors

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. Materials is an international peer-reviewed open access semimonthly 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.

• microstructure design of materials
• machine learning
• data-driven approaches
• optimization
• enhanced properties