Topic: Machine Learning/AI-Assisted Development of High-Performance Alloys

High-performance alloys form the cornerstone of numerous advanced technologies, playing critical roles in aerospace, automotive, energy, biomedical, and other industries. The development of these alloys, which require exceptional mechanical, thermal, and chemical properties, has traditionally relied on meticulous design, precise microstructural control, and optimal processing techniques. However, the increasing complexity of alloy demands now calls for more efficient and innovative approaches.

Artificial intelligence (AI) has emerged as a game-changer in the field of materials science, revolutionizing the design and development of high-performance alloys. AI accelerates alloy discovery and optimization, providing powerful tools to identify complex correlations, optimize processes, and explore vast compositional-microstructural spaces with unprecedented efficiency. Notably, cutting-edge algorithms such as active learning, generative models, and large language models (LLM) have opened new avenues in alloy design. These advanced AI approaches not only reduce the cost and time of alloy development but also facilitate the discovery of novel alloys with exceptional properties, effectively bridging the gap between theoretical design and practical applications.

In this Special Issue, we focus on the transformative impact of AI and state-of-the-art algorithms on the design of high-performance alloys. Contributions will highlight how AI-driven methods can address critical challenges in alloy research and development. We welcome both original research articles and insightful reviews on topics including, but not limited to:

● Development of high-throughput experimental and computational methods for creating high-quality alloy databases/datasets;

● Development of LLM models for materials and their applications in high-performance alloys;

● Development of AI-driven laboratories for alloy preparation and characterization;

● AI-enabled predictions of phase stability, thermodynamic properties, and kinetic behaviors in alloys;

● AI-driven composition design and screening for high-performance alloys;

● AI-driven approaches for optimizing microstructure, process parameters, and mechanical/functional properties;

● AI-driven acceleration of numerical simulations in alloys, such as molecular dynamics, phase-field modeling, and others;

● AI-driven tools for the accelerated discovery of novel high-performance alloys;

● Applications of AI in predicting mechanical, thermal, and functional properties, as well as the performance of alloys;

● Integration of AI methods with first-principles calculations, molecular dynamics, CALPHAD modeling, phase-field simulations, or multi-scale simulations.