Inorganic Material

Inorganic materials research aims to discover and design new materials with desired properties for technological applications. Current efforts heavily utilize machine learning, particularly generative models like diffusion models and transformers, to predict material properties (e.g., dielectric tensors) and even generate novel inorganic crystal structures based on desired characteristics. These advancements leverage large datasets and sophisticated algorithms to accelerate the traditionally slow and expensive process of materials discovery, impacting fields ranging from energy storage to electronics. The integration of structural constraints within generative models is a key focus, improving the efficiency and success rate of generating stable and functional materials.