An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Abstract Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of important but challenging problems materials is discover multiple polycrystalline microstructures with properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework elastic materials. In this work, represented by Orientation Distribution Function (ODF) determines volume densities crystallographic orientations. The was evaluated on two crystal systems, cubic hexagonal, Titanium (Ti) in Joint Automated Repository Various Integrated Simulations (JARVIS) database expected be widely applicable systems. proposed can without compromising property values saving significant computational time.