Topic: AI-accelerated Materials Microstructure Design: From First-Principles to Phase-Field Model

The rapid development of artificial intelligence (AI) is opening up new frontiers in materials science. In particular, AI-accelerated microstructure design is emerging as a powerful approach to bridge traditional physics-based simulations with data-driven insights, enabling faster and more accurate predictions of materials behavior. From first-principles calculations to mesoscale phase-field modeling, AI is transforming the way researchers explore, design, and optimize microstructures for advanced materials with tailored properties.

The design and evolution of materials microstructures are key to improving the performance and functionality of structural and functional materials. With the integration of AI techniques, such as machine learning and deep learning, researchers can now build predictive models, uncover hidden relationships in high-dimensional data, and automate simulation workflows. These advances are accelerating innovation in materials discovery, especially by connecting atomistic simulations to microstructure-level models in a more efficient and scalable manner.

This Special Issue focuses on the latest research at the intersection of AI and computational microstructure design. We welcome Original Research and Review articles that explore the integration of AI with first-principles methods, phase-field modeling, data-driven approaches, and multiscale simulations. Areas of interest include but are not limited to:

● AI-assisted first-principles calculations;

● Machine learning for property prediction from quantum calculations;

● AI-enhanced phase-field models and simulations;

● Microstructure evolution prediction under processing conditions;

● High-throughput screening and data mining for materials microstructure design;

● Multiscale modeling from atoms to microstructures;

● Interdisciplinary approaches combining AI with DFT, MD, and other computational methods;

● Case studies in AI-accelerated materials discovery and optimization.

AI in materials science, microstructure design, first-principles calculations, phase-field modeling, machine learning, data-driven materials design, multiscale modeling, materials optimization, predictive modeling.