Scope
------------------
The main focus of materials science is to study the complex relationship of “composition-process-structure-property” of materials. In the advent of the digital revolution, artificial intelligence (AI) has emerged as a powerful tool to accelerate the development of new materials and significantly reduce materials development costs. This special issue highlights the recent progress of novel AI-enhanced computational approaches that advance the state-of-the-art in property prediction, process optimization, and inverse design of new materials.


Topics of Interest
------------------
This special issue solicits original research, review articles, and commentary articles. Topics of interest include, but are not limited to:

Machine learning potentials for materials science
Density functional theory with machine learning
Quantum chemistry methods with machine learning
Quantum and classical dynamics with machine learning
Quantum Monte Carlo with machine learning
Phase field with machine learning
Finite element method with machine learning
Materials property prediction with machine learning
Inverse design of new materials with machine learning
Foundation Models/Large-language Models for materials science


Guest Editors
------------------
Prof. Yanjing Su, University of Science and Technology Beijing
Prof. Xiao He, East China Normal University
Prof. Naihua Miao, Beihang University
Prof. Yunhao Lu, Zhejiang University
Prof. Pavlo O. Dral, Xiamen University
Dr. Lipeng Chen, Zhejiang Lab



Submission Instructions
------------------
Please indicate in your cover letter that your submission is intended for inclusion in the special issue.

Submission Deadline: October 31, 2024