Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modelling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Recent advancements in generative artificial intelligence (AI), such as generative adversarial networks, diffusion models, variational autoencoder, and large language models, have revolutionized the material design process at multiple scale, enabling inverse design of materials with target properties and performance. In this presentation, I will explore the use of generative AI in designing materials at multiple scales, from atomic lattices to microscale structures and metamaterial architectures. Particularly, I will discuss how can we harness generative AI to extract materials information and generate materials from experimental data, such as transmission electron microscope and scanning electron microscope images. Also, I will introduce how generative AI can speed up the design of mechanical metamaterials with customized properties. I will end up with a brief outlook of how these AI-designed materials can be used for a wide range of applications, such as robotics.