AI for Material Science

In recent years, AI has made significant contributions to materials science. AI algorithms are being used to accelerate materials discovery and development processes. One area of focus is the prediction of new materials with desirable properties, such as high strength or conductivity, by analyzing large datasets of material properties and structures. AI is also being used to optimize material synthesis processes, helping researchers identify the best conditions for producing materials with specific properties. Additionally, AI is aiding in the design of new materials for applications in areas such as energy storage, catalysis, and electronics. As representative examples, this chapter provides brief introductions to selected advancements in AI for materials, including discovering materials via Bayesian Active Learning, Graph Networks, and designing autonomous laboratories by utilizing Active Learning with robots.