Abstract: In recent years the rapid growth of computer processing power has led to major breakthroughs in scientific computing and artificial intelligence. The deep integration of these two fields has jointly fostered a data-driven paradigm for scientific research. As a representative of artificial intelligence technology, machine learning has brought unprecedented opportunities for computational materials design, with current applications mainly focusing on property prediction, synthesis prediction, knowledge discovery, and generative inverse design. This article will briefly describe the research progress in this field, and look ahead to the future directions and challenges.