Integrating Data Science and Computational Materials Science

Submission Deadline: September 30, 2025Contribute to this Special Topic
This collection aims to explore how cutting-edge data-science techniques, such as machine learning, artificial intelligence, and big data analytics, can be integrated with computational materials science to accelerate materials discovery, optimize materials performance, and enhance predictive modeling. The convergence of these disciplines is unlocking new possibilities in materials design, enabling the handling of complex datasets, the discovery of hidden patterns, and the development of highly accurate models that push the boundaries of traditional computational approaches.

Topics covered include, but are not limited to:

  • New techniques that integrate data science and computational material science including explainable AI methods, graph neural networks, large language models, and reinforcement learning
  • Addressing current challenges to integrate computational material science and data science, and proposing new strategies for overcoming them
  • Accelerating computational materials science through machine learning to overcome the limitations of traditional computational techniques
  • Computational materials design and discovery to guide experimental investigations such as energy storage, sustainable technologies, and aerospace, etc.
  • Development of open-access data repositories, software, and techniques that assist computational materials science approaches promoting collaboration and transparency in the community
  • Standardization of materials data to ensure the reproducibility of computational models

Guest Editors

Dilpuneet S. Aidhy, Clemson University

Donghwa Lee, Pohang University of Science and Technology

Kamal Choudhary, National Institute of Standards and Technology

Submission Deadline: September 30, 2025Contribute to this Special Topic