🔒SCI: Call for Papers-Advances in Computational Materials Science: Focusing on Atomic-Scale Simulations and AI-Driven Innovations

Journal: CMC-Computers, Materials & Continua (SCI IF=2.0)

🔗SI URL: https://www.techscience.com/cmc/special_detail/computational_materials_science

📅 Submission Deadline: 10th May 2025

🌟 Guest Editors:

Prof. Jun Tan, College of Materials Science and Engineering, Chongqing University, Chongqing, 400044, China

Dr. Yunxuan Zhou, Lanxi Magnesium Materials Research Institute, Lanxi 321100, China

Dr. Hailian Wang, School of Materials and Energy, Southwest University, Chongqing, 400715, China

🔍 Summary:

This special issue focuses on recent advancements in Computational Materials Science, particularly at the electronic and atomic scales. The rapid development of computational methods is transforming the way materials are designed, with Density Functional Theory (DFT) and molecular dynamics simulations being key tools for investigating material properties at the atomic level. The integration of machine learning and artificial intelligence is accelerating materials discovery and enhancing simulation precision, making this area of research increasingly important.

The issue aims to highlight cutting-edge computational methods and machine learning techniques that are shaping the future of materials science. A significant goal is to foster interdisciplinary collaboration by bridging computational and experimental approaches through Integrated Computational Materials Engineering (ICME). It will focus on addressing challenges in the prediction, optimization, and validation of materials properties, providing a platform for sharing innovative research.

📑Suggested Themes:

· First-principles calculations (e.g., Density Functional Theory)

· Molecular dynamics simulations

· Machine learning and AI in materials design

· Simulation accuracy and efficiency improvements

· Integrated Computational Materials Engineering (ICME)

Key Words:

First-principles calculations, Molecular dynamics simulations, Machine learning, Integrated computational materials engineering