Topic: AI for Structural Materials: Theory and Design

Structural materials are integral to various technology sectors, including aerospace, automotive, energy, and infrastructure. They present unique challenges and opportunities for artificial intelligence (AI). The inherent complexity of structural materials — often involving multiscale interactions between composition, microstructure, and macroscopic performance — necessitates innovative theoretical frameworks. In parallel, AI-driven methods, such as machine learning (ML) and large language models (LLMs), have demonstrated exceptional potential for advancing the development of new frameworks and optimizing materials design processes.

This Special Issue spotlights cutting-edge research at the intersection of AI and the theory and design of structural materials. The main objectives are to (i) advance the theoretical understanding of structural materials, including phase transformations, defect properties, and deformation mechanisms, and (ii) facilitate the design of novel structural materials with enhanced properties.

Topics of interest include, but are not limited to:

● Applications of ML methods for atomistic simulations in structural materials;

● AI models for predicting mechanical properties, phase stability, and microstructural evolution;

● AI approaches for uncovering new materials laws or structure-property relationships;

● Development of generative AI models for the inverse design of structural materials.