In this paper, machine learning and optimization based on simulation data of crystal growth are discussed from the viewpoint of informatics application, introducing our application to solution growth of SiC crystal. First, general aspects of crystal growth process simulation and its informatics applications are described. Next, after an overview of the solution growth of SiC crystal, the process optimization of solution growth of SiC crystal using machine learning is described, including the prediction model of temperature and flow of the solution in the crucible, the optimization of geometry conditions, and the optimization of process conditions corresponding to time evolution. Next, application to other materials is described. Finally, this paper is summarized with future prospects.
