Topic: Machine Learning for Advanced Design and Applications of High-Performance Ceramics
A Special Issue of Journal of Materials Informatics
ISSN 2770-372X (Online)
Submission deadline: 20 May 2024
Special Issue Introduction
Additionally, the concept of the high entropy has been successfully introduced to the design of high-performance ceramics. By incorporating multiple principal elements, the design space and properties of ceramic materials can be effectively tuned. The chemical complexity, on the one hand, brings about unusual performance to high-entropy ceramics but, at the same time, poses a great challenge for the rational design of ceramics within the immersed space.
Machine learning has emerged as a powerful tool for solving complex problems in materials science and engineering. Its ability to analyze large amounts of data, identify hidden patterns, and generate predictive models has demonstrated great potential in revolutionizing the way we design, develop, and characterize high-performance ceramics. By leveraging machine learning techniques, researchers can accelerate the discovery and optimization of novel ceramic materials, as well as enhance their understanding of the underlying mechanisms governing their properties.
This Special Issue aims to provide a comprehensive understanding of the most recent advancements, challenges, and future directions in the field of high-performance ceramics, with an emphasis on the integration of machine learning methodologies, which brings together a collection of original research articles, reviews, and perspectives from leading experts in the field of ceramics and machine learning. The topics covered in this issue include, but are not limited to:
1. Machine learning-based approaches for the design and optimization of high-performance ceramic materials;
2. Application of machine learning or machine learning-assisted simulations in predicting mechanical, thermal, and electrical properties of ceramics;
3. Data-driven modeling of composition-processing-structure-property relationships in ceramics;
4. Advanced machine learning techniques for characterizing the microstructure and defects in ceramic materials;
5. Machine learning-assisted process control and quality assurance in ceramic manufacturing;
6. Development of high throughput experimental/computational methods for the establishment of high-fidelity databases.
For Author Instructions, please refer to https://www.oaepublish.com/jmi/author_instructions
For Online Submission, please login at https://oaemesas.com/login?JournalId=jmi&SpecialIssueId=JMI231123
Submission Deadline: 20 May 2024
Contacts: Mengyu Yang, Assistant Editor, JMI@oaepublish.com