Call for Papers
Scope of the Thematic Issue - Machine Learning Applications in Computational Materials Science and Engineering
The integration of machine learning (ML) with traditional computational methods such as Density Functional Theory (DFT), Molecular Dynamics (MD), and Finite Element Methods (FEM) presents a significant advancement in computational materials science. These methods, while powerful, are often computationally intensive and time-consuming. The advent of ML techniques offers a promising solution by accelerating simulations, reducing computational costs, and developing surrogate models for complex material systems. ML approaches can significantly enhance the prediction of mechanical, thermal, electrical, and other material properties, facilitating the optimization of material performance and the understanding of structure-property relationships through data-driven methods. Additionally, the development and utilization of materials databases integrated with ML for materials informatics enable efficient data mining and knowledge discovery, providing a robust platform for advancing materials science.This special issue invites papers that explore these transformative ML applications. We seek contributions on the integration of ML with traditional computational techniques, advancements in materials characterization through ML, and the development of ML-based surrogate models. Papers that address ML-driven property prediction and performance optimization, sustainable materials development, and innovations in recycling and green manufacturing are particularly welcome. Authors are encouraged to submit manuscripts that present original research, comprehensive reviews, or insightful case studies. We look forward to receiving your valuable contributions and advancing the frontiers of machine learning in computational materials science and engineering.
Guest Editors
Lead Guest Editor:
Dr. Ayesha Sohail
https://www.sydney.edu.au/science/about/our-people/academic-staff/ayesha-sohail.html
Guest Editors:
Prof. Cemil Tunc
https://avesis.yyu.edu.tr/cemiltunc
Prof. Farid Chighoub
https://scholar.google.fr/citations?user=0Qq9LAUAAAAJ&hl=fr
Submission Deadline: 31 March 2025